**Apache Airflow version**: 2.0.0rc3

**Kubernetes version (if you are using kubernetes)** (use `kubectl version`):

**Environment**:

- **Cloud provider or hardware configuration**: Linux
- **OS** (e.g. from /etc/os-release): Ubuntu
- **Kernel** (e.g. `uname -a`): 
- **Install tools**:
- **Others**:

**What happened**:

After shutting down the scheduler, while tasks were in running state, trying to restart the scheduler results in pk violations..

```[2020-12-15 22:43:29,673] {scheduler_job.py:1293} ERROR - Exception when executing SchedulerJob._run_scheduler_loop
Traceback (most recent call last):
  File "/home/jcoder/git/airflow_2.0/pyenv/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1277, in _execute_context
    cursor, statement, parameters, context
  File "/home/jcoder/git/airflow_2.0/pyenv/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 593, in do_execute
    cursor.execute(statement, parameters)
psycopg2.errors.UniqueViolation: duplicate key value violates unique constraint "dag_run_dag_id_run_id_key"
DETAIL:  Key (dag_id, run_id)=(example_task_group, scheduled__2020-12-14T04:31:00+00:00) already exists.
```


**What you expected to happen**:

Scheduler restarts and picks up where it left off.

**How to reproduce it**:

Set example dag ( I used task_group) to schedule_interval `* * * * *` and start the scheduler and let it run for a few minutes. 
Shut down the scheduler
Attempt to restart the scheduler

**Anything else we need to know**:
I came across this doing testing using the LocalExecutor in a virtual env. If no else is able to reproduce it, I'll try again in a clean virtual env.

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: