### 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)

only within a BackfillJob instance and will track the execution of tasks,
        e.g. running, skipped, succeeded, failed, etc. Information about the dag runs
        related to the backfill job are also being tracked in this structure,
        .e.g finished runs, etc. Any other status related information related to the
        execution of dag runs / tasks can be included in this structure since it makes
        it easier to pass it around.
        :param to_run: Tasks to run in the backfill
        :param running: Maps running task instance key to task instance object
        :param skipped: Tasks that have been skipped
        :param succeeded: Tasks that have succeeded so far
        :param failed: Tasks that have failed
        :param not_ready: Tasks not ready for execution
        :param deadlocked: Deadlocked tasks
        :param active_runs: Active dag runs at a certain point in time
        :param executed_dag_run_dates: Datetime objects for the executed dag runs
        :param finished_runs: Number of finished runs so far
        :param total_runs: Number of total dag runs able to run
        """
        to_run: dict[TaskInstanceKey, TaskInstance] = attr.ib(factory=dict)
        running: dict[TaskInstanceKey, TaskInstance] = attr.ib(factory=dict)
        skipped: set[TaskInstanceKey] = attr.ib(factory=set)
        succeeded: set[TaskInstanceKey] = attr.ib(factory=set)
        failed: set[TaskInstanceKey] = attr.ib(factory=set)
        not_ready: set[TaskInstanceKey] = attr.ib(factory=set)
        deadlocked: set[TaskInstance] = attr.ib(factory=set)
        active_runs: list[DagRun] = attr.ib(factory=list)
        executed_dag_run_dates: set[pendulum.DateTime] = attr.ib(factory=set)
        finished_runs: int = 0
        total_runs: int = 0
    def __init__(