Use time.monotonic() for duration checks in databricks provider

[magic-peach]

Duration and timeout comparisons should use time.monotonic() instead of time.time() to be immune to system clock adjustments (NTP, DST, etc.). This aligns with the project coding standard documented in AGENTS.md.

Changes:

• databricks_base.py: Replace time.time() with time.monotonic() in Azure metadata cache TTL checks (both sync and async versions)
• databricks.py trigger: Replace time.time() with time.monotonic() in the polling loop timeout check
• mixins.py: Replace time.time() with time.monotonic() in SQL statement execution timeout checks, and fix grammar error in comment ("important steps; if a query takes to log" → "important step; if a query takes too long")

Note: time.time() is correctly retained for token expiry comparisons (lines 337, 378, 640, 1021, 1066) because those compare against wall-clock Unix timestamps received from the Databricks server.

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Was generative AI tooling used to co-author this PR?

• Yes — Claude Code (Opus 4.7)

Generated-by: Claude Code (Opus 4.7) following the guidelines

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Drafted-by: Claude Code (Opus 4.7) (no human review before posting)

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[SameerMesiah97]

Besides failing CI, I think this may introduce the same issue that was previously fixed in the dbt Cloud provider (#67616).

In the deferrable path, end_time is computed in the worker and passed to DatabricksSQLStatementExecutionTrigger, where it is later compared by the trigger. If end_time is changed to time.monotonic(), the value is no longer valid across process boundaries because monotonic clocks are local to each process.

We previously had to revert a similar change in the dbt Cloud provider for this reason. Could we keep time.time() for the serialized end_time, and only use time.monotonic() for duration measurements that remain entirely within a single process? In other words, would it be better to just keep this change limited to just the hook and leave time.time() as is in the mixin and the trigger?