### 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)

for cur_dag_id in dag_ids:
            for cur_run_id in run_ids:
                
                dag_task_id_groups = task_id_groups[(cur_dag_id, cur_run_id)]
                dag_map_index_groups = map_index_groups[(cur_dag_id, cur_run_id)]
                if len(dag_task_id_groups) <= len(dag_map_index_groups):
                    for cur_task_id, cur_map_indices in dag_task_id_groups.items():
                        filter_condition.append(
                            and_(
                                TaskInstance.dag_id == cur_dag_id,
                                TaskInstance.run_id == cur_run_id,
                                TaskInstance.task_id == cur_task_id,
                                TaskInstance.map_index.in_(cur_map_indices),
                            )
                        )
                else:
                    for cur_map_index, cur_task_ids in dag_map_index_groups.items():
                        filter_condition.append(
                            and_(
                                TaskInstance.dag_id == cur_dag_id,
                                TaskInstance.run_id == cur_run_id,
                                TaskInstance.task_id.in_(cur_task_ids),
                                TaskInstance.map_index == cur_map_index,
                            )
                        )
        return or_(*filter_condition)
    @classmethod
    def ti_selector_condition(cls, vals: Collection[str | tuple[str, int]]) -> ColumnOperators: