reweight the combined metric for lukewarm and inapplicable metrics

[toschmidt]

The Combined score is a weighted geomean of load (10%), data size (10%), cold (20%) and hot (60%) ratios. That unfairly penalizes systems for metrics that don't apply to them, and lets lukewarm "cold" numbers (really warm queries) distort the cold component.

Unify the per-metric exclusion rules in a single metricExcludes() helper (stateless from load, in-memory from cold/combined/load, lukewarm from cold, missing data size from size) and reuse it everywhere:

• Cold Run metric: lukewarm systems are excluded from the ranking by default.
• Combined per-query baseline: the cold-run minimum excludes lukewarm / in-memory systems, so their warm "cold" numbers can't depress the baseline and inflate every true-cold system's cold ratio. min load time / min data size likewise exclude systems that don't qualify.
• Combined score: a metric that doesn't apply to a system is dropped and the remaining weights are renormalized, instead of feeding a bogus ratio. Lukewarm systems keep a cold component of 0 with its weight folded into hot (load 10% / size 10% / hot 80%); a stateless engine that still reports a load time (e.g. Polars (Parquet)) drops the load component; etc. The cold term is guarded so an all-lukewarm selection (empty cold baseline) can't poison the score with NaN.

[toschmidt]

@alexey-milovidov can you take a look at this?

[alexey-milovidov]

Depends on #1646

[toschmidt]

@alexey-milovidov I rebased the PR and changed the flag to no-cold