### 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)

tuple_in_condition,
    with_row_locks,
)
from airflow.utils.state import DagRunState, State, TaskInstanceState
from airflow.utils.types import DagRunType
if TYPE_CHECKING:
    from types import FrameType
    from airflow.dag_processing.manager import DagFileProcessorAgent
TI = TaskInstance
DR = DagRun
DM = DagModel
@dataclass
class ConcurrencyMap:
    """
    Dataclass to represent concurrency maps
    It contains a map from (dag_id, task_id) to # of task instances, a map from (dag_id, task_id)
    to # of task instances in the given state list and a map from (dag_id, run_id, task_id)
    to # of task instances in the given state list in each DAG run.
    """
    dag_active_tasks_map: dict[str, int]
    task_concurrency_map: dict[tuple[str, str], int]
    task_dagrun_concurrency_map: dict[tuple[str, str, str], int]
    @classmethod
    def from_concurrency_map(cls, mapping: dict[tuple[str, str, str], int]) -> ConcurrencyMap:
        instance = cls(Counter(), Counter(), Counter(mapping))
        for (d, r, t), c in mapping.items():
            instance.dag_active_tasks_map[d] += c
            instance.task_concurrency_map[(d, t)] += c
        return instance
def _is_parent_process() -> bool: