### 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 = dag_id
        self.run_id = run_id
        self.execution_date = execution_date
        self.start_date = start_date
        self.external_trigger = external_trigger
        self.conf = conf or {}
        if state is not None:
            self.state = state
        if queued_at is NOTSET:
            self.queued_at = timezone.utcnow() if state == State.QUEUED else None
        else:
            self.queued_at = queued_at
        self.run_type = run_type
        self.dag_hash = dag_hash
        self.creating_job_id = creating_job_id
        super().__init__()
    def __repr__(self):
        return (
            "<DagRun {dag_id} @ {execution_date}: {run_id}, state:{state}, "
            "queued_at: {queued_at}. externally triggered: {external_trigger}>"
        ).format(
            dag_id=self.dag_id,
            execution_date=self.execution_date,
            run_id=self.run_id,
            state=self.state,
            queued_at=self.queued_at,
            external_trigger=self.external_trigger,
        )
    @property
    def stats_tags(self) -> dict[str, str]: