**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/serialization/serialized_objects.py
"""Helper function of depth first search for deserialization."""
        
        if cls._is_primitive(encoded_var):
            return encoded_var
        elif isinstance(encoded_var, list):
            return [cls._deserialize(v) for v in encoded_var]
        if not isinstance(encoded_var, dict):
            raise ValueError(f"The encoded_var should be dict and is {type(encoded_var)}")
        var = encoded_var[Encoding.VAR]
        type_ = encoded_var[Encoding.TYPE]
        if type_ == DAT.DICT:
            return {k: cls._deserialize(v) for k, v in var.items()}
        elif type_ == DAT.DAG:
            return SerializedDAG.deserialize_dag(var)
        elif type_ == DAT.OP:
            return SerializedBaseOperator.deserialize_operator(var)
        elif type_ == DAT.DATETIME:
            return pendulum.from_timestamp(var)