When pyiceberg loads Iceberg tables containing large JSON data into memory, memory usage explodes in pyarrow

### Apache Iceberg version

0.11.0 (latest release)

### Please describe the bug 🐞

We have a table with a column containing large JSON strings (basically think of large `pydantic` validation results). The resulting datafile in Iceberg itself is a roughly 6MB (GZip compressed) Parquet file, but when querying it, the memory consumption goes to ~4GB. (Just for a few seconds, but long enough to cause out-of-memory issues on some systems.)

The issue is that by default `pyarrow` loads the strings per row into memory, which blows up the memory. If we download the datafile and open it directly via `pyarrow` this behaviour can be reproduced.

There are 2 workarounds in `pyarrow`: 1. Don't load the problematic column (given that is possible in your use case) and 2. switch to dictionary-encoding for set column (example snippet below).

```py
from pyarrow.parquet import read_table
table = read_table("datafile.parquet", read_dictionary=["problematic_column"])
```

Issue in `pyiceberg`:
Regardless if you use `table.scan(...).to_arrow()` or `table.scan(...).to_arrow_batch_reader()`, `pyiceberg` has afaik currently no option to specify the dictionary encoding for certain tables, hence `pyarrow` uses the default encoding and the memory usage explodes.

The `to_arrow_batch_reader` does not help here either, because -as per my understanding- in the batch reader of `pyiceberg` each batch represents an individual datafile. Hence, if there is one problematic 6MB datafile, it makes no difference if you use the batch reader or not. I also have the impression that when you iterate over the reader, `pyarrow` has already loaded the parquet file in a separate thread and this is where the memory explosion actually happens.

So the current only workaround in `pyiceberg` is option 1: Don't load the problematic column by specifying the `selected_fields`:

```py
from pyiceberg.catalog import load_catalog

catalog = load_catalog("default")
table = catalog.load_table("your_table")
reader = table.scan(selected_fields=("all", "other", "columns")).to_arrow_batch_reader()
for batch in reader:
...
```

Expected behaviour:
There should be an option somewhere, e.g. in the data_scan to specify for which columns dictionary encoding should be used. This option should be forwarded to `pyarrow` internally somehow, so that `pyarrow` uses less memory.

Remark:
I would not change the default behaviour. It would be just good to have the option to configure the encoding in pyarrow when needed.

This issue is a follow up for https://github.com/apache/iceberg-python/issues/1205

### Willingness to contribute

- [ ] I can contribute a fix for this bug independently
- [ ] I would be willing to contribute a fix for this bug with guidance from the Iceberg community
- [x] I cannot contribute a fix for this bug at this time

Start with the table.scan(...).to_arrow() and to_arrow_batch_reader() entry points, then trace how they invoke pyarrow. Compare this with pyarrow.parquet.read_table and its read_dictionary option. Done means callers can configure dictionary encoding for selected columns without changing the default behavior, with the option forwarded during reads.

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