### 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.task.params = context["params"]
            task_orig = self.render_templates(context=context)
            if not test_mode:
                rtif = RenderedTaskInstanceFields(ti=self, render_templates=False)
                RenderedTaskInstanceFields.write(rtif)
                RenderedTaskInstanceFields.delete_old_records(self.task_id, self.dag_id)
            
            airflow_context_vars = context_to_airflow_vars(context, in_env_var_format=True)
            os.environ.update(airflow_context_vars)
            
            
            
            if not self.next_method:
                self.log.info(
                    "Exporting env vars: %s",
                    " ".join(f"{k}={v!r}" for k, v in airflow_context_vars.items()),
                )
            
            self.task.pre_execute(context=context)
            
            self._run_execute_callback(context, self.task)
            
            with set_current_context(context):
                result = self._execute_task(context, task_orig)
            
            self.task.post_execute(context=context, result=result)
        Stats.incr(f"operator_successes_{self.task.task_type}", tags=self.stats_tags)
        Stats.incr("ti_successes", tags=self.stats_tags)
    def _run_finished_callback(