ISSUE FIX:- #6783

What this PR does / why we need it:

MetricsCalculator.compute_numeric() passed values straight to
np.histogram(), which raises on NaN/+-Inf:

ValueError: autodetected range of [0.05, inf] is not finite

Non-finite values arise from ordinary feature engineering - a
click-through rate whose denominator is zero, a log of zero, or missing
values encoded as NaN. compute_all() has no per-column error handling,
so a single such value discarded the metrics for the entire feature view,
including columns that were perfectly well-formed.

The failure was silent from the caller's perspective: the monitoring API
still returned HTTP 200 with "status": "completed" and
"computed_features": 0, so the UI showed no error - the feature view was
simply absent from the Monitoring page.

This PR filters non-finite values once, after nulls are dropped, so every
statistic is computed over finite data, and applies the existing
_safe_float/opt_float helpers to min_val, max_val and the
quantiles (previously only mean and stddev were guarded, letting Inf
leak into stored metrics). row_count/null_count continue to describe
the raw data.

The same unguarded np.histogram call is duplicated in the Dask offline
store's _dask_compute_numeric_metrics, and is fixed there too.

Verification

Before: a feature view containing one inf value produced zero metrics
for all of its features. After: full metrics are produced, computed
correctly over the finite values (verified against an identical feature
view with no inf, which behaved correctly on both sides).

Full unit suite: 315 passed, 24 skipped. 6 new regression tests added.

Which issue(s) this PR fixes:

Fixes #<ISSUE_NUMBER>

Checks

• I've made sure the tests are passing.
• My commits are signed off (git commit -s)
• My PR title follows conventional commits format

Testing Strategy

• Unit tests