### 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)

"""Sets the log context."""
        self.raw = raw
        self._set_context(self)
    @staticmethod
    def filter_for_tis(tis: Iterable[TaskInstance | TaskInstanceKey]) -> BooleanClauseList | None:
        """Returns SQLAlchemy filter to query selected task instances"""
        
        
        tis = list(tis)
        if not tis:
            return None
        first = tis[0]
        dag_id = first.dag_id
        run_id = first.run_id
        map_index = first.map_index
        first_task_id = first.task_id
        
        dag_ids, run_ids, map_indices, task_ids = set(), set(), set(), set()
        for t in tis:
            dag_ids.add(t.dag_id)
            run_ids.add(t.run_id)
            map_indices.add(t.map_index)
            task_ids.add(t.task_id)