Investigate Distributed Support

## Background

Running subimage registration tasks on a single workstation may require a prohibitively long time to run on massive, cloud-based image datasets. We would like to be able to distribute registration tasks among a cluster of worker nodes to execute in parallel.

The `itk_dreg` framework is built with distributed registration in mind via streaming readers and `dask.delayed` tasks. However, output serialization is not fully supported in ITK v5.4rc2 or earlier.

ITK v5.4rc3 wheels will include support for unbuffered ITK images introduced in https://github.com/InsightSoftwareConsortium/ITK/pull/4270. That support will allow us to serialize `itk.Image`s describing oriented bounding boxes over which piecewise `itk.Transform` results are be valid, which is required for distributed processing.

## Steps to Investigate

When ITK v5.4rc3 is available on PyPI:
1. Update `pyproject.toml` and CI workflows in `itk-dreg` to use the updated ITK version
2. Run the `localcluster` and `serialize_pairwise_result` tests locally and verify that both tests pass
3. Re-enable the `localcluster` and `serialize_pairwise_result` tests in CI and verify that automated tests pass

For further testing:
1. Use [`dask.distributed.LocalCluster`](https://docs.dask.org/en/stable/deploying-python.html#localcluster) to mock a distributed cluster on your local system. Run serialized registration in an example notebook on a `LocalCluster` and verify that tasks are visible in the accompanying Dask dashboard.
2. Set up access to a distributed cluster and test distributed registration on the cluster. (xref: [Coiled](https://www.coiled.io/), [ACCESS](https://access-ci.org/))
3. Explore [Dask optimization](https://docs.dask.org/en/stable/optimize.html) to reduce task serialization requirements