### 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)

TaskInstance.run_id,
            TaskInstance.state,
            TaskInstance._try_number,
            func.min(TaskInstanceNote.content).label("note"),
            func.count(func.coalesce(TaskInstance.state, sqla.literal("no_status"))).label("state_count"),
            func.min(TaskInstance.start_date).label("start_date"),
            func.max(TaskInstance.end_date).label("end_date"),
        )
        .join(TaskInstance.task_instance_note, isouter=True)
        .filter(
            TaskInstance.dag_id == dag.dag_id,
            TaskInstance.run_id.in_([dag_run.run_id for dag_run in dag_runs]),
        )
        .group_by(TaskInstance.task_id, TaskInstance.run_id, TaskInstance.state, TaskInstance._try_number)
        .order_by(TaskInstance.task_id, TaskInstance.run_id)
    )
    grouped_tis = {task_id: list(tis) for task_id, tis in itertools.groupby(query, key=lambda ti: ti.task_id)}
    def task_group_to_grid(item, grouped_tis, *, is_parent_mapped: bool):
        if not isinstance(item, TaskGroup):
            def _get_summary(task_instance):
                return {
                    "task_id": task_instance.task_id,
                    "run_id": task_instance.run_id,
                    "state": task_instance.state,
                    "start_date": task_instance.start_date,
                    "end_date": task_instance.end_date,
                    "try_number": wwwutils.get_try_count(task_instance._try_number, task_instance.state),
                    "note": task_instance.note,
                }
            def _mapped_summary(ti_summaries):