**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 pool_slots_free == 0:
            self.log.debug("All pools are full!")
            return executable_tis
        max_tis = min(max_tis, pool_slots_free)
        
        
        
        query = (
            session.query(TI)
            .outerjoin(TI.dag_run)
            .filter(or_(DR.run_id.is_(None), DR.run_type != DagRunType.BACKFILL_JOB))
            .join(TI.dag_model)
            .filter(not_(DM.is_paused))
            .filter(TI.state == State.SCHEDULED)
            .options(selectinload('dag_model'))
        )
        starved_pools = [pool_name for pool_name, stats in pools.items() if stats['open'] <= 0]
        if starved_pools:
            query = query.filter(not_(TI.pool.in_(starved_pools)))
        query = query.limit(max_tis)
        task_instances_to_examine: List[TI] = with_row_locks(
            query,
            of=TI,
            session=session,
            **skip_locked(session=session),
        ).all()
        
        
        if len(task_instances_to_examine) == 0:
            self.log.debug("No tasks to consider for execution.")