Add arrow_reader_clickbench benchmark

[alamb]

Which issue does this PR close?

• Closes arrow_reader_row_filter benchmark doesn't capture page cache improvements #7460
• Part of [EPIC] Faster performance for parquet predicate evaluation for non selective filters #7456

Rationale for this change

We are trying to improve the performance of row filter application in the Parquet arrow reader and part of that is a benchmark that we can use to guide optimization efforts.

However, as discussed in #7428 the arrow_reader_row_filter microbenchmark doesn't currently reflect the actual performance we see in our end to end application (DataFusion).

cargo bench --all-features --bench arrow_reader_row_filter

Thus, we think we need to create a benchmark that uses the actual ClickBench dataset with appropriate filtering

• See arrow_reader_row_filter benchmark doesn't capture page cache improvements #7460 for more details

What changes are included in this PR?

1. Adds a new arrow_reader_clickbench benchmark

This benchmark tests applying the actual clickbench filters (and column materialization):

1. Single file and partitioned (100 file) datasets
2. async and sync readers
3. All clickbench query patterns

Are there any user-facing changes?

New benchmark, and hopefully thus improved filter / projection performance

TODO

• Change String types to use Utf8View
• Add sync/async reader
• Add hits_partitioned / hits
• Complete other predicate types

[alamb]

This benchmark is now looking pretty nice -- it tests just the parquet reading and has all the query predicate patterns. Tomorrow I need to finish adding all the other query patterns and give it a final polish.