Support timestamp formats on CsvReadOptions or Schema

### Is your feature request related to a problem or challenge?

In Apache Spark it is possible to define what the expected timestamp format will be.

See `timestampFormat` and `timestampNTZFormat ` [csv options](https://spark.apache.org/docs/latest/sql-data-sources-csv.html)

This works for both user supplied and inferred schemas.

In datafusion similar functionality is only available when parsing CSV via [SQL DDL options](https://datafusion.apache.org/user-guide/sql/format_options.html)

Currently with datafusion that is not possible even for user supplied schemas. Parsing a CSV that has timestamps with non standard formats will result in an error. Example
CSV contains a column `created_at` with the following value `2025-06-23-05.07.34.214000`

Schema definition for the timestamp column:
```
Field::new("created_at", DataType::Timestamp(TimeUnit::Microsecond, None), true),
```
Attempting to parse CSV file
```
Error: ArrowError(ParseError("Error parsing column 11 at line 1: Parser error: Error parsing timestamp from '2025-06-23-05.07.34.214000': invalid timestamp separator"), None)
```

### Describe the solution you'd like

Users should be able to supply a [custom timestamp format](https://docs.rs/chrono/0.4.42/chrono/format/strftime/index.html#specifiers)

### Describe alternatives you've considered

Extend `CsvReadOptions` and allow users to supply a custom timestamp format. This would be similar to the Apache Spark approach. This format would be used to parse timestamps during schema inference or with user supplied schemas.

And, optionally, extend `DataType::Timestamp` to include a user defined timestamp. This would be more flexible as it would allow per column timestamp formats.

### Additional context

- Currenty the only workaround is to define non custom timetamps as being strings and convert them afterwards with extra code.
- It is possible to supply a timestamp format already when parsing with [SQL DDL](https://datafusion.apache.org/user-guide/sql/format_options.html)

Commencez par CsvReadOptions et le chemin d’analyse des timestamps CSV, puis comparez les options de format SQL DDL existantes. Déterminez comment un format personnalisé devrait s’appliquer à l’inférence du schéma et aux schémas fournis par l’utilisateur, notamment si DataType::Timestamp nécessite des modifications ; le travail est terminé lorsque les valeurs de timestamp non standard sont analysées correctement via les deux chemins.

Rédigé par le modèle d'indexation à partir du texte de l'issue.