### 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)

return prune_dict({"dag_id": self.dag_id, "run_type": self.run_type})
    @property
    def logical_date(self) -> datetime:
        return self.execution_date
    def get_state(self):
        return self._state
    def set_state(self, state: DagRunState):
        if state not in State.dag_states:
            raise ValueError(f"invalid DagRun state: {state}")
        if self._state != state:
            self._state = state
            self.end_date = timezone.utcnow() if self._state in State.finished else None
            if state == State.QUEUED:
                self.queued_at = timezone.utcnow()
    @declared_attr
    def state(self):
        return synonym("_state", descriptor=property(self.get_state, self.set_state))
    @provide_session
    def refresh_from_db(self, session: Session = NEW_SESSION) -> None: