**Apache Airflow version**: 2.0.1
**Kubernetes version**: v1.19.7
**Executor**: KubernetesExecutor

**What happened**:

If you have a worker that gets stuck in pending forever, say with a missing volume mount, the task will stay in the queued state forever. Nothing is applying a timeout on it actually being able to start.

**What you expected to happen**:

Eventually the scheduler will notice that the worker hasn't progressed past pending after a given amount of time and will mark it as a failure.

**How to reproduce it**:
```python
from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.utils.dates import days_ago
from kubernetes.client import models as k8s


default_args = {
    "owner": "airflow",
}

with DAG(
    dag_id="pending",
    default_args=default_args,
    schedule_interval=None,
    start_date=days_ago(2),
) as dag:
    BashOperator(
        task_id="forever_pending",
        bash_command="date; sleep 30; date",
        executor_config={
            "pod_override": k8s.V1Pod(
                spec=k8s.V1PodSpec(
                    containers=[
                        k8s.V1Container(
                            name="base",
                            volume_mounts=[
                                k8s.V1VolumeMount(mount_path="/foo/", name="vol")
                            ],
                        )
                    ],
                    volumes=[
                        k8s.V1Volume(
                            name="vol",
                            persistent_volume_claim=k8s.V1PersistentVolumeClaimVolumeSource(
                                claim_name="missing"
                            ),
                        )
                    ],
                )
            ),
        },
    )
```


**Anything else we need to know**:
Related to:

* #15149 (This is reporting that these pending pods don't get deleted via "Mark Failed")
* #14556 (This handles when these pending pods get deleted and is already fixed)

tests/executors/test_kubernetes_executor.py
'try_number': '1',
                        },
                    ),
                    spec=k8s.V1PodSpec(
                        containers=[
                            k8s.V1Container(
                                name="base",
                                image="airflow:3.6",
                                args=['airflow', 'tasks', 'run', 'true', 'some_parameter'],
                                env=[k8s.V1EnvVar(name='AIRFLOW_IS_K8S_EXECUTOR_POD', value='True')],
                            )
                        ],
                        image_pull_secrets=[k8s.V1LocalObjectReference(name='airflow-registry')],
                        scheduler_name='default-scheduler',
                        security_context=k8s.V1PodSecurityContext(fs_group=50000, run_as_user=50000),
                    ),
                )
            )
    @mock.patch('airflow.executors.kubernetes_executor.KubernetesJobWatcher')
    @mock.patch('airflow.executors.kubernetes_executor.get_kube_client')
    def test_change_state_running(self, mock_get_kube_client, mock_kubernetes_job_watcher):
        executor = self.kubernetes_executor
        executor.start()
        key = ('dag_id', 'task_id', 'ex_time', 'try_number1')
        executor._change_state(key, State.RUNNING, 'pod_id', 'default')
        assert executor.event_buffer[key][0] == State.RUNNING
    @mock.patch('airflow.executors.kubernetes_executor.KubernetesJobWatcher')
    @mock.patch('airflow.executors.kubernetes_executor.get_kube_client')
    @mock.patch('airflow.executors.kubernetes_executor.AirflowKubernetesScheduler.delete_pod')
    def test_change_state_success(self, mock_delete_pod, mock_get_kube_client, mock_kubernetes_job_watcher):