### Description

expanding tasks in batches to allow mapped tasks spawn more than 1024 processes.

### Use case/motivation

Maximum length of a list is limited to 1024 by `max_map_length (AIRFLOW__CORE__MAX_MAP_LENGTH)`.
during scheduling of the new tasks, an UPDATE query is ran that tries to set all the new tasks at once.  Increasing `max_map_length` more than 4K makes airflow scheduler completely unresponsive.

Also, Postgres throws `stack depth limit exceeded` error which can be fixed by updating to a newer version and setting `max_stack_depth` higher. But it doesn't really matter because airflow scheduler freezes up.
 
As a workaround, I split the dag runs into subdag runs which works but it would be much nicer if we didn't have to worry about exceeding  `max_map_length`. 

### Related issues

It was discussed here:
[Increasing 'max_map_length' leads to SQL 'max_stack_depth' error with 5000 dags to be spawned #28478](https://github.com/apache/airflow/discussions/28478)

### Are you willing to submit a PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

self,
        dag_id: str | None = None,
        run_id: str | None = None,
        queued_at: datetime | None | ArgNotSet = NOTSET,
        execution_date: datetime | None = None,
        start_date: datetime | None = None,
        external_trigger: bool | None = None,
        conf: Any | None = None,
        state: DagRunState | None = None,
        run_type: str | None = None,
        dag_hash: str | None = None,
        creating_job_id: int | None = None,
        data_interval: tuple[datetime, datetime] | None = None,
    ):
        if data_interval is None:
            
            self.data_interval_start = self.data_interval_end = None
        else:
            self.data_interval_start, self.data_interval_end = data_interval