**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
self.log.debug("Calling SchedulerJob.adopt_or_reset_orphaned_tasks method")
                try:
                    num_failed = (
                        session.query(SchedulerJob)
                        .filter(
                            SchedulerJob.state == State.RUNNING,
                            SchedulerJob.latest_heartbeat < (timezone.utcnow() - timedelta(seconds=timeout)),
                        )
                        .update({"state": State.FAILED})
                    )
                    if num_failed:
                        self.log.info("Marked %d SchedulerJob instances as failed", num_failed)
                        Stats.incr(self.__class__.__name__.lower() + '_end', num_failed)
                    resettable_states = [State.SCHEDULED, State.QUEUED, State.RUNNING]
                    query = (
                        session.query(TI)
                        .filter(TI.state.in_(resettable_states))
                        
                        
                        
                        
                        .outerjoin(TI.queued_by_job)
                        .filter(or_(TI.queued_by_job_id.is_(None), SchedulerJob.state != State.RUNNING))
                        .join(TI.dag_run)
                        .filter(
                            DagRun.run_type != DagRunType.BACKFILL_JOB,
                            
                            DagRun.state == State.RUNNING,
                        )
                        .options(load_only(TI.dag_id, TI.task_id, TI.execution_date))