### 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)

"""Returns dataset dependencies graph."""
        nodes_dict: dict[str, Any] = {}
        edge_tuples: set[dict[str, str]] = set()
        for dag, dependencies in SerializedDagModel.get_dag_dependencies().items():
            dag_node_id = f"dag:{dag}"
            if dag_node_id not in nodes_dict and len(dependencies) > 0:
                for dep in dependencies:
                    if dep.dependency_type == "dag" or dep.dependency_type == "dataset":
                        nodes_dict[dag_node_id] = node_dict(dag_node_id, dag, "dag")
                        if dep.node_id not in nodes_dict:
                            nodes_dict[dep.node_id] = node_dict(
                                dep.node_id, dep.dependency_id, dep.dependency_type
                            )
                        if dep.source != "dataset":
                            edge_tuples.add((f"dag:{dep.source}", dep.node_id))
                        if dep.target != "dataset":
                            edge_tuples.add((dep.node_id, f"dag:{dep.target}"))
        nodes = list(nodes_dict.values())
        edges = [{"source": source, "target": target} for source, target in edge_tuples]
        data = {
            "nodes": nodes,
            "edges": edges,
        }
        return (
            htmlsafe_json_dumps(data, separators=(",", ":"), dumps=flask.json.dumps),
            {"Content-Type": "application/json; charset=utf-8"},
        )
    @expose("/object/datasets_summary")
    @auth.has_access([(permissions.ACTION_CAN_READ, permissions.RESOURCE_DATASET)])
    def datasets_summary(self):