Expose PyIceberg table as PyArrow Dataset

### Feature Request / Improvement

Migrated from https://github.com/apache/iceberg/issues/7598:

Hi, I've been looking at seeing what we can do to make PyArrow Datasets extensible for various table formats and making them consumable to various compute engines (including DuckDB, Polars, DataFusion, Dask). I've written up my observations here: https://docs.google.com/document/d/1r56nt5Un2E7yPrZO9YPknBN4EDtptpx-tqOZReHvq1U/edit?usp=sharing

## What this means for PyIceberg's API

Currently, integration with engines like DuckDB means filters and projections have to be specified up front, rather than pushed down from the query:

```python
con = table.scan(
row_filter=GreaterThanOrEqual("trip_distance", 10.0),
selected_fields=("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime"),
).to_duckdb(table_name="distant_taxi_trips")
```

Ideally, we can export the table as a dataset, register it in DuckDB (or some other engine), and then filters and projections can be pushed down as the engine sees fit. Then the following would perform equivalent to the above, but would be more user friendly:

```python
dataset = table.to_pyarrow_dataset()
con.register(dataset, "distant_taxi_trips")
conn.sql(""""SELECT VendorID, tpep_pickup_datetime, tpep_dropoff_datetime
FROM distant_taxi_trips
WHERE trip_distance > 10.0""")
```

### Query engine

Other

Start with the existing table.scan(...).to_duckdb() integration and the PyArrow Dataset extensibility notes linked in the issue. Define how table.to_pyarrow_dataset() should expose the table to DuckDB or another engine, including filter and projection pushdown; done means the example query works without specifying those operations up front.

Escrita pelo modelo de indexação a partir do texto da issue.