**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
task_instance.pool_slots,
                        open_slots,
                        pool,
                    )
                    num_starving_tasks += 1
                    num_starving_tasks_total += 1
                    
                    continue
                executable_tis.append(task_instance)
                open_slots -= task_instance.pool_slots
                dag_concurrency_map[dag_id] += 1
                task_concurrency_map[(task_instance.dag_id, task_instance.task_id)] += 1
            Stats.gauge(f'pool.starving_tasks.{pool_name}', num_starving_tasks)
        Stats.gauge('scheduler.tasks.starving', num_starving_tasks_total)
        Stats.gauge('scheduler.tasks.running', num_tasks_in_executor)
        Stats.gauge('scheduler.tasks.executable', len(executable_tis))
        task_instance_str = "\n\t".join([repr(x) for x in executable_tis])
        self.log.info("Setting the following tasks to queued state:\n\t%s", task_instance_str)
        
        filter_for_tis = TI.filter_for_tis(executable_tis)
        session.query(TI).filter(filter_for_tis).update(
            
            
            {TI.state: State.QUEUED, TI.queued_dttm: timezone.utcnow(), TI.queued_by_job_id: self.id},
            synchronize_session=False,
        )
        for ti in executable_tis:
            make_transient(ti)
        return executable_tis
    def _enqueue_task_instances_with_queued_state(self, task_instances: List[TI]) -> None: