Fix normalized multiple-choice probability underflow

[YusefSyed]

Summary

• stabilize NormalizedMultiChoiceProbability by subtracting the largest
  finite log-probability before exponentiation
• preserve the existing behavior for non-finite and empty inputs
• add a regression for long-sequence log-probabilities that previously
  underflowed to zero

Why

The metric currently computes exp(logprob) before dividing by the sum. For
finite sequence log-likelihoods such as [-1000.0, -1001.0], both exponentials
underflow to zero. The metric then returns 0.0 and emits an invalid-divide
warning, even though the gold choice has normalized probability
1 / (1 + exp(-1)), approximately 0.7310586.

Subtracting one shared finite maximum is algebraically neutral for the
probability ratios and prevents both underflow and unnecessary overflow.

Verification

Red-on-main control at 932e1f2f:

result=0.0
warning_count=1
invalid value encountered in divide

Patched branch:

result=0.7310585786300049
warning_count=0

Checks:

• pytest tests/test_unit_base_metrics.py -q -k 'mc_probability_metric' — 2 passed
• pytest tests/test_unit_base_metrics.py -q -k 'not f1_dynamic_metric and not exact_match_dynamic_metric' — 8 passed, 3 skipped
• PYTHONPATH=. pytest tests/unit/metrics/test_normalizations.py -q — 4 passed
• ruff format --check on changed files — passed
• ruff check on changed files — passed
• git diff --check — passed

The two excluded tests require the optional multilingual extra. A separate
metrics request run also reaches the repository's existing xxhash 4 string
compatibility failure tracked in #1330; neither failure touches this metric or
diff.

Closes #1375.