[EPIC] A collection of support for metadata columns in ListingTable

Other systems support "metadata" columns when querying datasources. These metadata columns do not exist in the underlying data source but instead are related to the source

Common examples:
* Row number
* File name
* Row Group Number (parquet)

## DataBricks / Spark

It appears DataBricks / spark represents this concept as a struct column `_metadata` column with multiple fields
https://docs.databricks.com/aws/en/ingestion/file-metadata-column

```sql
SELECT
*
,_metadata
,_metadata.file_path
,_metadata.file_name
,_metadata.file_modification_time
FROM
json.`/path/to/table/data`
```

It looks like maybe spark/databricks used to support the `input_file_name()` function, but has moved to `_metadata`: https://pawankumarshukla1979.medium.com/tips-use-metadata-instead-of-input-file-name-function-in-databricks-runtime-10-5-and-above-b32766b0296b

## DuckDB
DuckDB seems to model this as additional parameters to the `read_parquet` function, specifically `file_name` and `file_row_number`:
https://duckdb.org/docs/stable/data/parquet/overview#parameters

Per @adriangb [last year](https://github.com/apache/datafusion/issues/15173#issuecomment-2858707706):
```
D select filename, sum(row_count) as row_count from read_parquet('/Users/adriangb/Downloads/data2/**/*_stats.parquet', filename=true) group by filename order by row_count desc limit 10;
Binder Error:
Option filename adds column "filename", but a column with this name is also in the file. Try setting a different name: filename=''
```

Related tickets for adding similar metadata features to DataFusion:
- [ ] https://github.com/apache/datafusion/issues/13975
- [ ] https://github.com/apache/datafusion/issues/15173
- [x] https://github.com/apache/datafusion/issues/6051
- [ ] https://github.com/apache/datafusion/issues/20132
- [x] https://github.com/apache/datafusion/issues/13261
- [ ] https://github.com/apache/datafusion/issues/18482

Es werden keine Quelldateien, Tests oder Einstiegspunkte für die Implementierung genannt. Beginne damit, die verknüpften DataFusion-Tickets sowie die Metadatenbeispiele von Spark und DuckDB zu prüfen, und kläre anschließend den gewünschten Umfang der Metadatenspalten und die Akzeptanzkriterien, bevor du Implementierungsarbeiten identifizierst.

Vom Indexierungsmodell aus dem Issue-Text verfasst.