ci: freeze setup-python interpreter

[anthonychengit]

What changes were proposed in this PR?

Prevent Renovate from independently raising the Python interpreter used by GitHub Actions while the CI data stack remains pinned to older pandas, NumPy, and PyArrow releases.

Before: setup-python bump -> interpreter may outrun frozen package wheels -> CI install failure
After:  interpreter stays frozen -> interpreter and data packages are upgraded together manually

The rule is narrowly scoped to the github-actions manager and Renovate's python dependency name. Other GitHub Actions updates remain enabled and grouped as before.

Any related issues, documentation, discussions?

Closes #7720.

How was this PR tested?

The regression check was written before the config change and initially failed because no setup-python freeze rule existed. After the change:

npx --yes --package renovate@44.46.4 renovate-config-validator .github/renovate.json5
git diff --check

Result: Renovate reported Config validated successfully against 1 file(s), and the diff check passed.

Was this PR authored or co-authored using generative AI tooling?

Generated-by: Codex was used for implementation and verification assistance. I reviewed the final code and test output before submission.

[github-actions]

Automated Reviewer Suggestions

Based on the git blame history of the changed files, we recommend the following reviewers:

• Contributors with relevant context: @Yicong-Huang
  You can notify them by mentioning @Yicong-Huang in a comment.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 93.24%. Comparing base (444fc58) to head (16530be).

Additional details and impacted files

@@            Coverage Diff            @@
##               main    #8004   +/-   ##
=========================================
  Coverage     93.24%   93.24%           
  Complexity     4677     4677           
=========================================
  Files          1177     1177           
  Lines         47685    47685           
  Branches       5312     5312           
=========================================
  Hits          44466    44466           
  Misses         1743     1743           
  Partials       1476     1476           

Flag	Coverage Δ		*Carryforward flag
access-control-service	81.00% <ø> (ø)
agent-service	99.32% <ø> (ø)
amber	89.40% <ø> (ø)		Carriedforward from 444fc58
computing-unit-managing-service	73.67% <ø> (ø)
config-service	86.73% <ø> (ø)
file-service	86.70% <ø> (ø)		Carriedforward from 444fc58
frontend	95.67% <ø> (ø)		Carriedforward from 444fc58
notebook-migration-service	79.31% <ø> (ø)
pyamber	97.78% <ø> (ø)
workflow-compiling-service	77.19% <ø> (ø)

*This pull request uses carry forward flags. Click here to find out more.

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[github-actions]

⚠️ Benchmark changes need a look

🟢 1 better · 🔴 2 worse · ⚪ 12 noise (<±5%) · 0 without baseline

Compared against main 444fc58 benchmarked on this same runner, so the delta is largely free of cross-runner hardware noise. The "7d avg" column still reflects the gh-pages dashboard. Treat <±5% as noise unless repeated.

Dashboard · Run

	config	throughput	MB/s	latency	max Δ latest / 7d
🟢	bs=10 sw=10 sl=64	416	0.254	23,116/30,664/30,664 us	🟢 -8.8% / 🔴 +121.5%
🔴	bs=100 sw=10 sl=64	956	0.583	102,511/145,162/145,162 us	🔴 +15.3% / 🔴 +55.0%
⚪	bs=1000 sw=10 sl=64	1,115	0.68	898,948/960,824/960,824 us	⚪ within ±5% / 🔴 -7.0%

Baseline details

Latest main 444fc58 from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	416 tuples/sec	418 tuples/sec	894.86 tuples/sec	-0.5%	-53.5%
bs=10 sw=10 sl=64	MB/s	0.254 MB/s	0.255 MB/s	0.546 MB/s	-0.4%	-53.5%
bs=10 sw=10 sl=64	p50	23,116 us	25,349 us	11,139 us	-8.8%	+107.5%
bs=10 sw=10 sl=64	p95	30,664 us	29,994 us	13,843 us	+2.2%	+121.5%
bs=10 sw=10 sl=64	p99	30,664 us	29,994 us	16,839 us	+2.2%	+82.1%
bs=100 sw=10 sl=64	throughput	956 tuples/sec	980 tuples/sec	1,166 tuples/sec	-2.4%	-18.0%
bs=100 sw=10 sl=64	MB/s	0.583 MB/s	0.598 MB/s	0.711 MB/s	-2.5%	-18.0%
bs=100 sw=10 sl=64	p50	102,511 us	102,157 us	87,509 us	+0.3%	+17.1%
bs=100 sw=10 sl=64	p95	145,162 us	125,870 us	93,675 us	+15.3%	+55.0%
bs=100 sw=10 sl=64	p99	145,162 us	125,870 us	102,153 us	+15.3%	+42.1%
bs=1000 sw=10 sl=64	throughput	1,115 tuples/sec	1,122 tuples/sec	1,198 tuples/sec	-0.6%	-6.9%
bs=1000 sw=10 sl=64	MB/s	0.68 MB/s	0.685 MB/s	0.731 MB/s	-0.7%	-7.0%
bs=1000 sw=10 sl=64	p50	898,948 us	885,365 us	859,766 us	+1.5%	+4.6%
bs=1000 sw=10 sl=64	p95	960,824 us	989,206 us	902,897 us	-2.9%	+6.4%
bs=1000 sw=10 sl=64	p99	960,824 us	989,206 us	937,957 us	-2.9%	+2.4%

Raw CSV

config_idx,batch_size,schema_width,string_len,num_batches,total_ms,total_tuples,total_bytes,tuples_per_sec,mb_per_sec,lat_p50_us,lat_p95_us,lat_p99_us
0,10,10,64,20,480.65,200,128000,416,0.254,23115.69,30663.72,30663.72
1,100,10,64,20,2092.17,2000,1280000,956,0.583,102510.53,145161.70,145161.70
2,1000,10,64,20,17939.76,20000,12800000,1115,0.680,898947.74,960823.61,960823.61