`to_device` desynchronizes spatial data between CPU and GPU versions of array

## Describe the bug
When arrays are initialized on the CPU and passed to the GPU via `ClimaCore.to_device`, the spatial data (e.g., `grid`, `grid.full_grid`, etc.) appears to be copied independently, resulting in the loss of internal consistency. Consequently, data layouts that should be equivalent are evaluated as `false`, leading to bugs during subsequent operations or comparisons.

In particular, when layout-dependent operations like `column_reduce!` and `column_accumulate!` are used with the GPU version of the input arrays.

## To Reproduce
The following code reproduces the issue and shows how the spatial data becomes inconsistent after calling `to_device`. In this minimal example, `Fields.level(axes(...), 1) == axes(...)` evaluates to `true` on the CPU but fails post-transfer, which should not occur.

I could also push this as a unit test on ClimaAtmos, if that is how I should do it.

```julia
using ClimaCore:
Fields, Geometry, Meshes, Quadratures

using ClimaCore
using ClimaCore.CommonSpaces
import ClimaAtmos as CA
import ClimaComms

using CUDA

const FT = Float64

comms_ctx = ClimaComms.SingletonCommsContext()

(; config_file, job_id) = CA.commandline_kwargs()
config = CA.AtmosConfig(config_file; job_id, comms_ctx)

config.parsed_args["topography"] = "Earth";
config.parsed_args["topo_smoothing"] = false;
config.parsed_args["mesh_warp_type"] = "Linear";
(; parsed_args) = config

# Create meshes and spaces
h_elem = 16
nh_poly = 3
z_max = 42e3
z_elem = 33
dz_bottom = 300.0
radius = 6.371229e6

quad = Quadratures.GLL{nh_poly + 1}()
horizontal_mesh = CA.cubed_sphere_mesh(; radius, h_elem)
h_space = CA.make_horizontal_space(horizontal_mesh, quad, comms_ctx, false)
z_stretch = Meshes.HyperbolicTangentStretching(dz_bottom)
center_space, face_space =
CA.make_hybrid_spaces(h_space, z_max, z_elem, z_stretch; parsed_args)

ᶜlocal_geometry = Fields.local_geometry_field(center_space)
ᶠlocal_geometry = Fields.local_geometry_field(face_space)

# create Y
Yc = map(ᶜlocal_geometry) do lg
return (; ρ = FT(1.0), u_phy = FT(0), v_phy = FT(0), T = FT(0), qt = FT(0))
end
Yf = map(ᶠlocal_geometry) do lg
return (; u₃ = Geometry.Covariant3Vector(FT(0), lg))
end
Y = Fields.FieldVector(c = Yc, f = Yf)

ᶜz = Fields.coordinate_field(Y.c).z

z_level = similar(Fields.level(ᶜz, 1), FT)

# Prints True
print("Axes check on the CPU: ")
println(Fields.level(axes(ᶜz),1) == axes(z_level))

A = ClimaCore.to_device(ClimaComms.CUDADevice(),ᶜz)
B = ClimaCore.to_device(ClimaComms.CUDADevice(),z_level)

# Should also print True, but prints False
# This has the implication that column_accumulate
# and column_reduce do not work on inputs with
# mixed VIJFH and IJFH fields.
print("Axes check on the GPU: ")
println(Fields.level(axes(A), 1) == axes(B))

# Let's look at what is wrong...
C = Fields.level(axes(A),1)
D = axes(B)

# Prints True as it should be...
print("C == D: ")
println(getfield(C.grid.full_grid.face_local_geometry, :array) == getfield(D.grid.full_grid.face_local_geometry, :array))

print("C === D: ")
# ... but physical equivalence evaluates to False
println(getfield(C.grid.full_grid.face_local_geometry, :array) === getfield(D.grid.full_grid.face_local_geometry, :array))
```

Project

I am using the `.buildkite` project:
```
# Project.toml
[deps]
ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63"
ArtifactWrappers = "a14bc488-3040-4b00-9dc1-f6467924858a"
BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
ClimaAnalysis = "29b5916a-a76c-4e73-9657-3c8fd22e65e6"
ClimaAtmos = "b2c96348-7fb7-4fe0-8da9-78d88439e717"
ClimaComms = "3a4d1b5c-c61d-41fd-a00a-5873ba7a1b0d"
ClimaCore = "d414da3d-4745-48bb-8d80-42e94e092884"
ClimaCoreSpectra = "c2caaa1d-32ae-4754-ba0d-80e7561362e9"
ClimaCoreTempestRemap = "d934ef94-cdd4-4710-83d6-720549644b70"
ClimaDiagnostics = "1ecacbb8-0713-4841-9a07-eb5aa8a2d53f"
ClimaReproducibilityTests = "e0c89595-00ba-42a9-9f9b-061ef3dc23a1"
ClimaTimeSteppers = "595c0a79-7f3d-439a-bc5a-b232dc3bde79"
ClimaUtilities = "b3f4f4ca-9299-4f7f-bd9b-81e1242a7513"
Debugger = "31a5f54b-26ea-5ae9-a837-f05ce5417438"
DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
Infiltrator = "5903a43b-9cc3-4c30-8d17-598619ec4e9b"
Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953"
JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
Krylov = "ba0b0d4f-ebba-5204-a429-3ac8c609bfb7"
MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"
NCDatasets = "85f8d34a-cbdd-5861-8df4-14fed0d494ab"
NullBroadcasts = "0d71be07-595a-4f89-9529-4065a4ab43a6"
OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
Poppler_jll = "9c32591e-4766-534b-9725-b71a8799265b"
PrecompileCI = "76d61242-8ec2-4c91-8455-3234246697a2"
PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
Profile = "9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"
ProfileCanvas = "efd6af41-a80b-495e-886c-e51b0c7d77a3"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
SnoopCompile = "aa65fe97-06da-5843-b5b1-d5d13cad87d2"
SnoopCompileCore = "e2b509da-e806-4183-be48-004708413034"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"

[compat]
CairoMakie = "0.11, 0.12"
ClimaCoreSpectra = "0.1"
ClimaCoreTempestRemap = "0.3"
JET = "0.9"
PrettyTables = "2"
ProfileCanvas = "0.1"
SnoopCompileCore = "3"
julia = "1.10"
```

```
The Manifest.toml is too long. Including it as a `.txt` attachment.
[Manifest-v1.11.txt](https://github.com/user-attachments/files/20474817/Manifest-v1.11.txt)
```

## System details

Any relevant system information:
- Julia version:
```bash
╰─$ julia --version
julia version 1.11.5
```
- operating system:
```bash
Description: Manjaro Linux
Release: 25.0.3
```
- modules loaded on cluster (`module list`)
```
╰─$ module list
No Modulefiles Currently Loaded.
```

## Related issues / PRs
Appears to be related to #2312 and #2260 .

I found the `to_device` function here: https://clima.github.io/ClimaCore.jl/dev/faq/#Moving-objects-between-the-CPU-and-GPU.

Start with the ClimaCore.to_device entry point and reproduce the CPU/GPU axes comparison in the issue. Read the linked FAQ and related issues #2312 and #2260, then check the column_reduce! and column_accumulate! cases. Done means transferred spatial data remains internally consistent and the reported axes comparison succeeds on the GPU.

Written by the indexing model from the issue text.