**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
}
                )
            tis_changed = (
                session.query(models.TaskInstance)
                .filter(
                    models.TaskInstance.dag_id == subq.c.dag_id,
                    models.TaskInstance.task_id == subq.c.task_id,
                    models.TaskInstance.execution_date == subq.c.execution_date,
                )
                .update(ti_prop_update, synchronize_session=False)
            )
        if tis_changed > 0:
            session.flush()
            self.log.warning(
                "Set %s task instances to state=%s as their associated DagRun was not in RUNNING state",
                tis_changed,
                new_state,
            )
            Stats.gauge('scheduler.tasks.without_dagrun', tis_changed)
    @provide_session
    def __get_concurrency_maps(
        self, states: List[str], session: Session = None
    ) -> Tuple[DefaultDict[str, int], DefaultDict[Tuple[str, str], int]]:
        """
        Get the concurrency maps.
        :param states: List of states to query for
        :type states: list[airflow.utils.state.State]
        :return: A map from (dag_id, task_id) to # of task instances and
         a map from (dag_id, task_id) to # of task instances in the given state list
        :rtype: tuple[dict[str, int], dict[tuple[str, str], int]]