[Python] Deflake anomaly transform tests

[bvolpato]

Related to #37779.

The ensemble tests compare each row with an online Z-score calculated in input order, but each detector branch reshuffles independently. Use explicit score fields for grouping and aggregation assertions so valid reordering does not change the expected outputs. These tests still check features, thresholds, keys, examples, model IDs, scores, prediction counts, and AnyVote aggregation.

Keep the existing single-detector Z-score tests and add keyed and unkeyed ensemble tests using real Z-score models on constant observations. The new tests assert exactly two warmup predictions per model and key, regardless of processing order, so lost model state still fails the suite.

The extra state coverage matters because #37779 shows repeated warmup predictions that reordering alone does not explain. This PR removes an invalid ordering assumption and preserves checks for lost model state; it does not establish the cause of that historical runner failure.

Validation:

• 16 targeted tests pass in transforms_test.py and detectors/zscore_test.py.
• Reversing the input fails all four original ensemble cases and passes their updated versions.
• Both new state tests fail when model state is deliberately discarded.
• YAPF, Ruff, and git diff --check pass.

No changelog entry is needed for this test-only change.

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[bvolpato]

Run Python_ML PreCommit 3.13

[bvolpato]

Run Python_ML PreCommit 3.10

[github-actions]

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