Generalize jmh-table.py into a reusable visualization for any JMH benchmark

### Description

Spinoff from #15950 as @mikemccand suggested. The `jmh-table.py` visualization introduced in this PR for ScoreDoc sort benchmark is really cool, it's already a near-general solution.

One enhancement is: the script currently hardcodes `size` and `distribution` which ties specifically to `ScoreDocSortBenchmark`. It would be better to generalize this into a reusable tool that works with any of lucene's JMH benchmarks by auto-discovering `@Param` names.

As a motivating example, @gsmiller's PartitionByLeafBenchmark in #15938 has different params and he had to manually feed results to AI to build a better visual summary table. A generalized `jmh-table.py` as a *Skill*-ish tool would have worked out of the box.

Start with the existing jmh-table.py visualization introduced for the ScoreDoc sort benchmark, then compare its hardcoded size and distribution handling with the parameters in PartitionByLeafBenchmark. Generalize parameter handling by discovering JMH @Param names so the tool works across Lucene JMH benchmarks, and verify that it produces useful summary tables for benchmarks with different parameters.

Written by the indexing model from the issue text.