Simplify linear axis assignment logic

I split this PR from my metadata PR (https://github.com/imagej/pyimagej/pull/247). This PR aims to simply how we assign calibrated linear axes to datasets. Currently we check if net.imagej.axis.EnumeratedAxis is available and use that as our first choice. If net.imagej.axis.EnumeratedAxis is not available, then we fall back to net.imagej.axis.DefaultLinearAxis. This is fine for most things, but unfortunately this logic has some unintended consequences. @gselzer informed me that the BoneJ plugin actually checks for net.imagej.axis.DefaultLinearAxis and is unable to use the net.imagej.axis.EnumeratedAxis. The changes this PR brings are the following:

Drop net.imagej.axis.EnumeratedAxis from the axes assignment logic. While I would like to keep the option to use both, I don't realistically see how a user would indicate they want EnumeratedAxis or not. Also if they actually care about that, then it seems likely that they may have enough knowledge to change the CalibratedAxis type. Are there instances where ImageJ2 will produce a net.imagej.Dataset with net.imagej.axis.EnumeratedAxis? If not, then I think that is another point for dropping this behavior.
Resolve singleton dimension scale bug. I thought I got this into main already but I realize it has been passed around from one stale PR to another. Here it is! This change resolves a bug where getting the scale of a singleton dimension fails. This is because we calculate the slope from the dimensions coordinate array. The first two values are used for this math, which of course fails for singleton dimensions because their coordinate array is size 1. Thus all singleton dimensions should have their scale set to 1.

update 1

Thanks @gselzer for adding in a good way to have both EnumeratedAxis and DefaultLinearAxis as options based on coordinate array linearity.

Things for me to do:

TODO:

[x] Write tests for new scale logic.

update 2

I've added the following changes:

Remove the Finally block and slightly reworked the logic. Finally was always triggered and replaced the EnumeratedAxis with DefaultLinearAxis.
Added tests for:
- linear coords -> DefaultLinearAxis
- non-linear coords -> EnumeratedAxis
- non-numeric coords -> DefaultLinearAxis

Codecov Report

Patch coverage: 90.62% and project coverage change: +0.42 :tada:

Comparison is base (179f482) 76.93% compared to head (de50af9) 77.36%.

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Additional details and impacted files

```diff @@ Coverage Diff @@ ## main #265 +/- ## ========================================== + Coverage 76.93% 77.36% +0.42% ========================================== Files 16 16 Lines 1869 1869 ========================================== + Hits 1438 1446 +8 + Misses 431 423 -8 ``` | [Impacted Files](https://app.codecov.io/gh/imagej/pyimagej/pull/265?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=imagej) | Coverage Δ | | |---|---|---| | [src/imagej/dims.py](https://app.codecov.io/gh/imagej/pyimagej/pull/265?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=imagej#diff-c3JjL2ltYWdlai9kaW1zLnB5) | `65.73% <86.95%> (+2.62%)` | :arrow_up: | | [src/imagej/\_java.py](https://app.codecov.io/gh/imagej/pyimagej/pull/265?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=imagej#diff-c3JjL2ltYWdlai9famF2YS5weQ==) | `88.76% <100.00%> (+1.26%)` | :arrow_up: |

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