### 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,
        unfinished_tis: Sequence[TI],
        finished_tis: list[TI],
        session: Session,
    ) -> tuple[bool, bool]:
        dep_context = DepContext(
            flag_upstream_failed=True,
            ignore_in_retry_period=True,
            ignore_in_reschedule_period=True,
            finished_tis=finished_tis,
        )
        
        
        return (
            any(ut.are_dependencies_met(dep_context=dep_context, session=session) for ut in unfinished_tis),
            dep_context.have_changed_ti_states,
        )
    def _emit_true_scheduling_delay_stats_for_finished_state(self, finished_tis: list[TI]) -> None: