### 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)

if not dag:
                self.log.error("DAG '%s' not found in serialized_dag table", dag_model.dag_id)
                continue
            dag_hash = self.dagbag.dags_hash.get(dag.dag_id)
            data_interval = dag.get_next_data_interval(dag_model)
            
            
            
            
            
            
            
            
            if (dag.dag_id, dag_model.next_dagrun) not in existing_dagruns:
                dag.create_dagrun(
                    run_type=DagRunType.SCHEDULED,
                    execution_date=dag_model.next_dagrun,
                    state=DagRunState.QUEUED,
                    data_interval=data_interval,
                    external_trigger=False,
                    session=session,
                    dag_hash=dag_hash,
                    creating_job_id=self.job.id,
                )
                active_runs_of_dags[dag.dag_id] += 1
            if self._should_update_dag_next_dagruns(dag, dag_model, active_runs_of_dags[dag.dag_id]):
                dag_model.calculate_dagrun_date_fields(dag, data_interval)
        
        
    def _create_dag_runs_dataset_triggered(