### 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)

"""
        Detects datasets that are no longer referenced in any DAG schedule parameters or task outlets and
        sets the dataset is_orphaned flag to True
        """
        orphaned_dataset_query = (
            session.query(DatasetModel)
            .join(
                DagScheduleDatasetReference,
                isouter=True,
            )
            .join(
                TaskOutletDatasetReference,
                isouter=True,
            )
            
            
            .group_by(DatasetModel if session.get_bind().dialect.name == "mssql" else DatasetModel.id)
            .having(
                and_(
                    func.count(DagScheduleDatasetReference.dag_id) == 0,
                    func.count(TaskOutletDatasetReference.dag_id) == 0,
                )
            )
        )
        updated_count = sum(self._set_orphaned(dataset) for dataset in orphaned_dataset_query)
        Stats.gauge("dataset.orphaned", updated_count)