### Apache Airflow version

main (development)

### What happened

Task failing with error "missing upstream values" when running a dag with dynamic task group mapping
error -
```
[2022-11-23, 15:50:07 UTC] {abstractoperator.py:456} ERROR - Cannot expand <Task(_PythonDecoratedOperator): increment_and_verify.hello_there> for run manual__2022-11-23T15:49:59.771915+00:00; missing upstream values: ['x']
```

### What you think should happen instead

It should succeed 

### How to reproduce

Run the below dag code 
```
from airflow import DAG
from airflow.models.taskinstance import TaskInstance
from airflow.operators.dummy import DummyOperator
from datetime import datetime, timedelta
from airflow.decorators import task, task_group
with DAG(
    dag_id="taskmap_taskgroup",
    tags=["AIP_42"],
    start_date=datetime(1970, 1, 1),
    schedule_interval=None,
) as dag:

    @task
    def onetwothree():
        return [1,2,3]

    @task
    def hello_there(arg):
        print(arg)

    @task_group
    def increment_and_verify(x):
        hello_there(x)

    increment_and_verify.expand(x=onetwothree())>>DummyOperator(task_id="done")
```

### Operating System

mac os

### Versions of Apache Airflow Providers

_No response_

### Deployment

Virtualenv installation

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

"""
        Returns whether a task is in UP_FOR_RETRY state and its retry interval
        has elapsed.
        """
        
        return self.state == State.UP_FOR_RETRY and not self.ready_for_retry()
    @provide_session
    def are_dependents_done(self, session: Session = NEW_SESSION) -> bool:
        """
        Checks whether the immediate dependents of this task instance have succeeded or have been skipped.
        This is meant to be used by wait_for_downstream.
        This is useful when you do not want to start processing the next
        schedule of a task until the dependents are done. For instance,
        if the task DROPs and recreates a table.
        :param session: SQLAlchemy ORM Session
        """
        task = self.task
        if not task.downstream_task_ids:
            return True
        ti = session.query(func.count(TaskInstance.task_id)).filter(
            TaskInstance.dag_id == self.dag_id,
            TaskInstance.task_id.in_(task.downstream_task_ids),
            TaskInstance.run_id == self.run_id,
            TaskInstance.state.in_([State.SKIPPED, State.SUCCESS]),
        )
        count = ti[0][0]
        return count == len(task.downstream_task_ids)
    @provide_session
    def get_previous_dagrun(