**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)

airflow/jobs/scheduler_job.py
if (dag.dag_id, dag_model.next_dagrun) not in active_dagruns:
                run = dag.create_dagrun(
                    run_type=DagRunType.SCHEDULED,
                    execution_date=dag_model.next_dagrun,
                    start_date=timezone.utcnow(),
                    state=State.RUNNING,
                    external_trigger=False,
                    session=session,
                    dag_hash=dag_hash,
                    creating_job_id=self.id,
                )
                expected_start_date = dag.following_schedule(run.execution_date)
                if expected_start_date:
                    schedule_delay = run.start_date - expected_start_date
                    Stats.timing(
                        f'dagrun.schedule_delay.{dag.dag_id}',
                        schedule_delay,
                    )
        self._update_dag_next_dagruns(dag_models, session)
        
        
    def _update_dag_next_dagruns(self, dag_models: Iterable[DagModel], session: Session) -> None: