### 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, session: Session = NEW_SESSION, execute_callbacks: bool = True
    ) -> tuple[list[TI], DagCallbackRequest | None]:
        """
        Determines the overall state of the DagRun based on the state
        of its TaskInstances.
        :param session: Sqlalchemy ORM Session
        :param execute_callbacks: Should dag callbacks (success/failure, SLA etc.) be invoked
            directly (default: true) or recorded as a pending request in the ``returned_callback`` property
        :return: Tuple containing tis that can be scheduled in the current loop & `returned_callback` that
            needs to be executed
        """
        
        callback: DagCallbackRequest | None = None
        class _UnfinishedStates(NamedTuple):
            tis: Sequence[TI]
            @classmethod
            def calculate(cls, unfinished_tis: Sequence[TI]) -> _UnfinishedStates:
                return cls(tis=unfinished_tis)
            @property
            def should_schedule(self) -> bool:
                return (
                    bool(self.tis)
                    and all(not t.task.depends_on_past for t in self.tis)
                    and all(t.task.max_active_tis_per_dag is None for t in self.tis)
                    and all(t.task.max_active_tis_per_dagrun is None for t in self.tis)
                    and all(t.state != TaskInstanceState.DEFERRED for t in self.tis)
                )
            def recalculate(self) -> _UnfinishedStates: