[Python] Speed up ApproximateUnique accumulator merging

[sliortega295-ops]

Summary

• reuse the first ApproximateUnique accumulator instead of allocating an empty heap and reinserting every sampled hash
• preserve the empty-iterable behavior and leave later accumulators unchanged
• add merge identity, result, non-mutation, and empty-input coverage
• document the improvement in CHANGES.md

This brings the Python merge path in line with the existing Java implementation and removes one accumulator's worth of heap insertions from every merge.

Fixes #19459.

Validation

• pytest apache_beam/transforms/stats_test.py: 87 passed with the source implementation
• rebuilt the Python extensions, then reran the same suite: 87 passed
• focused new merge tests: 2 passed in both source and extension-backed runs
• Ruff, YAPF, and git diff --check: passed
• CPU microbenchmark, two identical full accumulators (median of 101 runs):
  • sample size 1,600: 0.6172 ms -> 0.2158 ms (2.86x)
  • sample size 10,000: 3.9099 ms -> 1.3734 ms (2.85x)

No GPU was used or required.

AI disclosure

The implementation, tests, benchmark, and this description were generated with OpenAI Codex at the account owner's request. Codex re-checked the final diff against the repository code and ran the validations listed above. No separate human line-by-line code review was performed before submission.

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R: @damccorm for label python.

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