**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
.filter(
                DagRun.dag_id.in_([o.dag_id for o in dag_models]),
                DagRun.state == State.RUNNING,  
                DagRun.external_trigger.is_(False),
            )
            .group_by(DagRun.dag_id)
            .all()
        )
        for dag_model in dag_models:
            
            
            try:
                dag = self.dagbag.get_dag(dag_model.dag_id, session=session)
            except SerializedDagNotFound:
                self.log.exception("DAG '%s' not found in serialized_dag table", dag_model.dag_id)
                continue
            active_runs_of_dag = active_runs_of_dags.get(dag.dag_id, 0)
            if dag.max_active_runs and active_runs_of_dag >= dag.max_active_runs:
                self.log.info(
                    "DAG %s is at (or above) max_active_runs (%d of %d), not creating any more runs",
                    dag.dag_id,
                    active_runs_of_dag,
                    dag.max_active_runs,
                )
                dag_model.next_dagrun_create_after = None
            else:
                dag_model.next_dagrun, dag_model.next_dagrun_create_after = dag.next_dagrun_info(
                    dag_model.next_dagrun
                )
    def _schedule_dag_run(