### 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)

"""Generates a URL to the Graph view for a DagRun."""
    dag_id = attr.get("dag_id")
    run_id = attr.get("run_id")
    execution_date = attr.get("dag_run.execution_date") or attr.get("execution_date")
    url = url_for("Airflow.graph", dag_id=dag_id, run_id=run_id, execution_date=execution_date)
    return Markup('<a href="{url}">{run_id}</a>').format(url=url, run_id=run_id)
def _get_run_ordering_expr(name: str) -> ColumnOperators:
    expr = DagRun.__table__.columns[name]
    
    
    
    
    
    if name in ("data_interval_start", "data_interval_end"):
        expr = func.coalesce(expr, DagRun.execution_date)
    return expr.desc()
def sorted_dag_runs(query: Query, *, ordering: Sequence[str], limit: int) -> Sequence[DagRun]:
    """Produce DAG runs sorted by specified columns.
    :param query: An ORM query object against *DagRun*.
    :param ordering: Column names to sort the runs. should generally come from a
        timetable's ``run_ordering``.
    :param limit: Number of runs to limit to.
    :return: A list of DagRun objects ordered by the specified columns. The list
        contains only the *last* objects, but in *ascending* order.
    """
    ordering_exprs = (_get_run_ordering_expr(name) for name in ordering)
    runs = query.order_by(*ordering_exprs, DagRun.id.desc()).limit(limit).all()
    runs.reverse()
    return runs
def format_map_index(attr: dict) -> str: