Allow calculating geometric mean of groups of benchmarks based on tags

[Moved from https://github.com/faster-cpython/ideas/discussions/395]

It's becoming obvious that:

• The pyperformance suite needs more benchmarks that are more similar to real-world workloads, and we should lean into optimizing for these and using these to report progress.
• Microbenchmarks of a particular feature are also useful and belong in the benchmark suite, but we shouldn't over-optimize for them or use them as a (misleading) indicator of overall progress.

It seems that one way to address this would be to lean into "tags" more in the pyperformance/pyperf ecosystem. pyperformance already allows for tags in each benchmark's pyproject.yaml.

I propose we:

1. Output the tags for each benchmark in the benchmark results in the metadata dictionary.
2. pyperf compare_to would then calculate the geometric mean for each subset of benchmarks for each tag found in the results, as well as "all" benchmarks (existing behavior). This could be behind a flag if backward compatibility matters.

Alternatives:

We could instead use the nested benchmark heirarchy, rather than tags. Personally, I think tags is easier to understand and more flexible (a benchmark could be associated with multiple tags).

Start by tracing how benchmark tags from pyperformance's pyproject.yaml are represented in benchmark results and metadata, then inspect pyperf's compare_to flow. Done means tags are exposed in metadata and geometric means are calculated for each tag subset as well as all benchmarks, with backward-compatibility behavior resolved.