**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
pools = models.Pool.slots_stats(session=session)
        for pool_name, slot_stats in pools.items():
            Stats.gauge(f'pool.open_slots.{pool_name}', slot_stats["open"])
            Stats.gauge(f'pool.queued_slots.{pool_name}', slot_stats[State.QUEUED])  
            Stats.gauge(f'pool.running_slots.{pool_name}', slot_stats[State.RUNNING])  
    @provide_session
    def heartbeat_callback(self, session: Session = None) -> None:
        Stats.incr('scheduler_heartbeat', 1, 1)
    @provide_session
    def adopt_or_reset_orphaned_tasks(self, session: Session = None):
        """
        Reset any TaskInstance still in QUEUED or SCHEDULED states that were
        enqueued by a SchedulerJob that is no longer running.
        :return: the number of TIs reset
        :rtype: int
        """
        self.log.info("Resetting orphaned tasks for active dag runs")
        timeout = conf.getint('scheduler', 'scheduler_health_check_threshold')
        for attempt in run_with_db_retries(logger=self.log):
            with attempt:
                self.log.debug(
                    "Running SchedulerJob.adopt_or_reset_orphaned_tasks with retries. Try %d of %d",
                    attempt.retry_state.attempt_number,
                    MAX_DB_RETRIES,
                )