Pyarrow multi-threading support for struct-list parquet file reads

Nobody has claimed this yet.

As originally discussed in #414 , it was discovered that pyarrow has multi-threaded parquet reading of columns enabled by default (which propagates to pandas read times when using pyarrow as the engine). However, while this multi-threading works for separate list-array columns, it doesn't work for loading separate columns stored within a single list struct. This is not something that we can change on our end, but this issue will log some reproducer code:

File Generation:

```
# Code block to generate needed parquet files
from nested_pandas.datasets import generate_data

# Generate a parquet dataset with struct-list format
nf = generate_data(100,2000, seed=1)[["nested"]]
nf.to_parquet("nested_parquet.parquet")

# Generate a parquet dataset with list-array format
nf["nested"].to_lists().to_parquet("list_parquet.parquet")
```

Versioning & Storage Context
```
import pyarrow as pa
pa.__version__
> '22.0.0'

# struct of lists storage as read by pyarrow
pa.parquet.read_table("nested_parquet.parquet").field("nested")
> pyarrow.Field, flux: list, band: list>>

# list storage as read by pyarrow
pa.parquet.read_table("list_parquet.parquet").field("t")
> pyarrow.Field>
```

Single-Thread Timings:

Multi-Thread Timings:

We see that multi-threading improves the read speed for list-arrays, but not for struct-list formatted data.

Start with the reproducer in issue #421, generating nested_parquet.parquet and list_parquet.parquet with nested_pandas.datasets.generate_data, then compare pyarrow 22.0.0 read timings and schemas. Determine whether the limitation can be addressed in this project or requires an upstream pyarrow report; done when the support path and expected behavior are documented.

Written by the indexing model from the issue text.