### Apache Airflow version

2.5.2

### What happened

when trying to delete a dag run with many tasks (>1000) the operation times out and the dag run is not deleted. 

### What you think should happen instead

_No response_

### How to reproduce

attempt to delete a dag run that contains >1000 tasks (in my case 10k) using the dagrun/list/ page results in a timeout:
![image](https://user-images.githubusercontent.com/7373236/226325567-5a87efa1-4744-417e-9995-b97dd1791401.png)


code for dag (however it fails on any dag with > 1000 tasks):
```
import json

from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator
from datetime import datetime, timedelta
from airflow.decorators import dag, task

default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'retries': 0,
    'retry_delay': timedelta(minutes=1),
    'start_date': datetime(2023, 2, 26),
    'is_delete_operator_pod': True,
    'get_logs': True
}


@dag('system_test', schedule=None, default_args=default_args, catchup=False, tags=['maintenance'])
def run_test_airflow():
    stress_image = 'dockerhub.prod.evogene.host/progrium/stress'  

    @task
    def create_cmds():
        commands = []
        for i in range(10000):
            commands.append(["stress --cpu 4 --io 1 --vm 2 --vm-bytes 6000M --timeout 60s"])
        return commands

    KubernetesPodOperator.partial(
        image=stress_image ,
        task_id=f'test_airflow',
        name=f'test_airflow',
        cmds=["/bin/sh", "-c"],
        log_events_on_failure=True,
        pod_template_file=f'/opt/airflow/dags/repo/templates/cpb_cpu_4_mem_16'
    ).expand(arguments=create_cmds())


run_test_airflow()

```

### Operating System

kubernetes deployment

### Versions of Apache Airflow Providers

_No response_

### Deployment

Official Apache Airflow Helm Chart

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit 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)

task_ids: list[str | tuple[str, int]] = []
        end_date = execution_date if not future else None
        start_date = execution_date if not past else None
        locked_dag_run_ids: list[int] = []
        if group_id is not None:
            task_group_dict = dag.task_group.get_task_group_dict()
            task_group = task_group_dict.get(group_id)
            if task_group is None:
                return redirect_or_json(
                    origin, msg=f"TaskGroup {group_id} could not be found", status="error", status_code=404
                )
            task_ids = task_ids_or_regex = [t.task_id for t in task_group.iter_tasks()]
            
            
            dag_runs_query = session.query(DagRun.id).filter(DagRun.dag_id == dag_id).with_for_update()
            if start_date is None and end_date is None:
                dag_runs_query = dag_runs_query.filter(DagRun.execution_date == start_date)
            else:
                if start_date is not None:
                    dag_runs_query = dag_runs_query.filter(DagRun.execution_date >= start_date)
                if end_date is not None:
                    dag_runs_query = dag_runs_query.filter(DagRun.execution_date <= end_date)
            locked_dag_run_ids = dag_runs_query.all()
        elif task_id:
            if map_indexes is None:
                task_ids = [task_id]
            else:
                task_ids = [(task_id, map_index) for map_index in map_indexes]
            task_ids_or_regex = [task_id]
        dag = dag.partial_subset(