**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_to_reset_or_adopt = with_row_locks(
                        query, of=TI, session=session, **skip_locked(session=session)
                    ).all()
                    to_reset = self.executor.try_adopt_task_instances(tis_to_reset_or_adopt)
                    reset_tis_message = []
                    for ti in to_reset:
                        reset_tis_message.append(repr(ti))
                        ti.state = State.NONE
                        ti.queued_by_job_id = None
                    for ti in set(tis_to_reset_or_adopt) - set(to_reset):
                        ti.queued_by_job_id = self.id
                    Stats.incr('scheduler.orphaned_tasks.cleared', len(to_reset))
                    Stats.incr('scheduler.orphaned_tasks.adopted', len(tis_to_reset_or_adopt) - len(to_reset))
                    if to_reset:
                        task_instance_str = '\n\t'.join(reset_tis_message)
                        self.log.info(
                            "Reset the following %s orphaned TaskInstances:\n\t%s",
                            len(to_reset),
                            task_instance_str,
                        )
                    
                    
                    session.flush()
                except OperationalError:
                    session.rollback()
                    raise
        return len(to_reset)