[Test][Core] Cover Mamba-deferred prefix-cache metrics

[mo-ke-ke]

Purpose

Add scheduler regression coverage for the Mamba same-step cache deferral path
after #48860 moved prefix-cache statistics recording from lookup time to request
admission.

Hybrid attention/Mamba requests can be looked up more than once when a Mamba
state block cached by a sibling request in the same scheduler step temporarily
prevents admission. These retries are not preemptions. The new test verifies
that each request and its prompt tokens are counted exactly once despite those
repeated lookups.

The existing hybrid scheduler helper is generalized so the test can select
mamba_cache_mode="align" without attaching a KV connector.

Why this is not a duplicate

No open PR references #48860 or covers Mamba same-step deferral for
prefix-cache metrics. #48970 tests hybrid-Mamba output correctness for #43559
with GPU end-to-end workloads; it does not test scheduler metric accounting.
#48668 fixed statistics dropped by zero-output offline-engine steps, whereas
this test guards against repeated lookup accounting before admission.

Test plan and results

On a Linux CPU pod at d8f840071:

PYTHONPATH=/local-ssd/vllm .venv/bin/python -m pytest \
  tests/v1/core/test_scheduler.py -v \
  -k "prefix_cache_query_not_inflated or preemption_re_records_prefix_cache_query or prefix_cache_stats_not_recorded_when_caching_disabled or external_prefix_cache_metrics or hybrid_per_group_hit_divergence or hybrid_fa_deeper_hit"

Result: 13 passed, 135 deselected.

The new test also passed after restoring the current implementation. For a
negative control, moving statistics recording back to lookup time as before
#48860 made it fail with (requests, queries) == (5, 256) instead of the
expected (3, 144).

uvx ruff check tests/v1/core/test_scheduler.py
uvx ruff format --check tests/v1/core/test_scheduler.py
git diff --check

All checks passed. Model evaluation does not apply because this change only
adds unit-test coverage and does not modify runtime code or model output.

AI assistance

AI assistance was used for code exploration, test migration, validation, and
drafting this description. The human submitter will review every changed line
before marking this PR ready for review.

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