BUG: SourceMorph spacing-attribute inconsistent

Start at the compute_source_morph entry point and reproduce the two calls from the issue with oct6 and ico5 source spaces. Trace how src_to and spacing determine SourceMorph.spacing, then add or update coverage so the result matches the target source-space spacing in both directions.

Description of the problem

When src_to is supplied to compute_source_morph and is has a spacing other then "ico5", the spacing-attribute of the resulting SourceMorph doesn't seem to be consistent (see example below)

Steps to reproduce

import mne
from mne.datasets import sample

data_path = sample.data_path()
subjects_dir = data_path / "subjects"

# oct6 spacing
src_from = mne.read_source_spaces(subjects_dir / "sample" / "bem" / "sample-oct-6-src.fif")
subject_from = "sample"
# ico5 spacing
src_to = mne.read_source_spaces(subjects_dir / "fsaverage" / "bem" / "fsaverage-ico-5-src.fif")
subject_to = "fsaverage"
morph = mne.compute_source_morph(
    src_from,
    subject_from=subject_from,
    subject_to=subject_to,
    src_to=src_to,
    subjects_dir=subjects_dir,
)
assert morph.spacing == 5

# When you switch src_from and src_to, the spacing should be 6 since the target source-space has a spacing of oct6.
# But it is still 5.
morph = mne.compute_source_morph(
    src_to,
    subject_from=subject_to,
    subject_to=subject_from,
    src_to=src_from,
    subjects_dir=subjects_dir,
)
# This fails
assert morph.spacing == 6

Link to data

No response

Expected results

SourceMorph.spacing being consistent with spacing of src_to

Actual results

It's always 5 (or the value currently supplied to spacing, which should be ignored when src_to is supplied)

Additional information

Platform Windows-10-10.0.22621-SP0
Python 3.11.6 | packaged by conda-forge | (main, Oct 3 2023, 10:29:11) [MSC v.1935 64 bit (AMD64)]

Core
├☑ mne 1.6.0.dev24+g22ffe6f7a
├☑ numpy 1.24.4 (OpenBLAS 0.3.24 with 32 threads)
├☑ scipy 1.11.3
├☑ matplotlib 3.8.0 (backend=QtAgg)
├☑ pooch 1.7.0
└☑ jinja2 3.1.2

Numerical (optional)
├☑ sklearn 1.3.1
├☑ numba 0.57.1
├☑ nibabel 5.1.0
├☑ nilearn 0.10.2
├☑ dipy 1.7.0
├☑ openmeeg 2.5.6
├☑ pandas 2.1.1
└☐ unavailable cupy

Visualization (optional)
├☑ pyvista 0.42.3 (OpenGL 4.6.0 Compatibility Profile Context 23.9.3.230915 via AMD Radeon(TM) Graphics)
├☑ pyvistaqt 0.0.0
├☑ vtk 9.2.6
├☑ qtpy 2.4.0 (PyQt5=5.15.8)
├☑ ipympl 0.9.3
├☑ pyqtgraph 0.13.3
├☑ mne-qt-browser 0.6.0.dev13+g481264d
└☐ unavailable ipyvtklink

Ecosystem (optional)
├☑ mne-bids 0.14.dev0
├☑ mne-connectivity 0.6.0dev0
└☐ unavailable mne-nirs, mne-features, mne-icalabel, mne-bids-pipeline