### 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)

dag_run: DagRun | None, *, json_encoder: type[json.JSONEncoder] = json.JSONEncoder
) -> dict[str, Any] | None:
    if not dag_run:
        return None
    conf, conf_is_json = get_dag_run_conf(dag_run.conf, json_encoder=json_encoder)
    return {
        "run_id": dag_run.run_id,
        "queued_at": datetime_to_string(dag_run.queued_at),
        "start_date": datetime_to_string(dag_run.start_date),
        "end_date": datetime_to_string(dag_run.end_date),
        "state": dag_run.state,
        "execution_date": datetime_to_string(dag_run.execution_date),
        "data_interval_start": datetime_to_string(dag_run.data_interval_start),
        "data_interval_end": datetime_to_string(dag_run.data_interval_end),
        "run_type": dag_run.run_type,
        "last_scheduling_decision": datetime_to_string(dag_run.last_scheduling_decision),
        "external_trigger": dag_run.external_trigger,
        "conf": conf,
        "conf_is_json": conf_is_json,
        "note": dag_run.note,
    }
def check_import_errors(fileloc, session):
    
    import_errors = session.query(errors.ImportError).filter(errors.ImportError.filename == fileloc).all()
    if import_errors:
        for import_error in import_errors:
            flash("Broken DAG: [{ie.filename}] {ie.stacktrace}".format(ie=import_error), "dag_import_error")
def check_dag_warnings(dag_id, session):