High (resident) memory usage when fetching Parquet metadata/schema

We have a code to fetch parquet schema from a file using pyarrow, here's a minimal example:

```py
import pyarrow.parquet as pq

with open("/tmp/part.snappy.parquet", mode="rb") as fd:
s = pq.read_schema(fd)
```

That example file is about 288MB, we've notice that the resident memory usage of this code spikes close to 500MB:

Is this expected that to fetch schema, we need to allocate so much memory? Worth noting that this memory is eventually freed up. Should some arguments be tweaked or is this a bug somewhere?

```sh
> du -sh /tmp/part.snappy.parquet
288M /tmp/part.snappy.parquet
```

Versions (py 3.10):
```
> conda list | grep arrow
arrow-cpp 12.0.0 hce30654_0_cpu conda-forge
libarrow 12.0.0 h3b4cbd9_0_cpu conda-forge
pyarrow 12.0.0 py310h7c67832_0_cpu conda-forge
```

### Component(s)

Python

Start by reproducing the minimal pyarrow.parquet.read_schema example with the supplied 288MB file and the listed Arrow 12.0.0/Python 3.10 versions, while measuring resident memory. Compare the allocation and release behavior with the expected metadata-read path; done means determining whether the spike is expected or identifying a reproducible defect and its relevant component.

Written by the indexing model from the issue text.