EDM Memory usage

The peak memory consumption of the track state EDM strongly increases when pileup and even complexity is increased. For ttbar events at $\mu=200$ from Pythia8, it can go up to a few gigabytes per event, which is suboptimal.

This issue tracks progress to improve and optimize the memory consumption.

## Associated PRs:

- #1507
This helps centralize the `MultiTrajectory` usage: a single instance is used by the examples algorithms to accumulate tracks from all seeds
- #1512
Switches from overallocating vectors and matrices for the measurements to using a jagged vector structure that looks like this:
```
| x x x | x | x x | ...
^ ^ ^
M1,D=3 M2,D=1 M3,D=2
```
- #1511
Implements statistics on the memory usage of `Acts::VectorMultiTrajectory` to give us an idea of what's going on.

## Status quo
The tooling from #1511 gives the following measurements from `MultiTrajectory`:
### ODD full chain example with Pythia8 ttbar @ mu200
```
other:
count: 1877425.000000
index: 128.91M
parPred: 85.94M
covPred: 515.65M
parFilt: 0.00M
covFilt: 0.00M
parSmth: 85.94M
covSmth: 515.65M
meas: 25.08M
measCov: 150.46M
jac: 515.65M
sourceLinks: 18.50M
projectors: 4.18M
total: 2045.97M
meas:
count: 653022.00
index: 44.84M
parPred: 27.47M
covPred: 164.82M
parFilt: 29.89M
covFilt: 179.36M
parSmth: 29.89M
covSmth: 179.36M
meas: 29.89M
measCov: 179.36M
jac: 164.82M
sourceLinks: 9.96M
projectors: 4.98M
total: 1044.66M
```
### ODD truth tracking with Pythia8 ttbar @ mu200
```
count: 40146.000000
index: 2.76M
parPred: 1.84M
covPred: 11.03M
parFilt: 0.00M
covFilt: 0.00M
parSmth: 1.84M
covSmth: 11.03M
meas: 0.00M
measCov: 0.00M
jac: 11.03M
sourceLinks: 0.31M
projectors: 0.00M
total: 39.82M
meas:
count: 37159.00
index: 2.55M
parPred: 1.70M
covPred: 10.21M
parFilt: 1.70M
covFilt: 10.21M
parSmth: 1.70M
covSmth: 10.21M
meas: 1.70M
measCov: 10.21M
jac: 10.21M
sourceLinks: 0.57M
projectors: 0.28M
total: 61.24M
```

## Other ideas
- [ ] Switch to float for storage of parameters and covariances to float
- [ ] Only store lower left triangle of covariance matrices. @tboldagh points out there is actually a triangular view [directly in Eigen](https://github.com/acts-project/acts/issues/1516#issue-1372460714)

Start with the MultiTrajectory usage in the examples algorithms and the memory statistics tooling from #1511, then review the remaining ideas in this issue, including float storage and triangular covariance views. Done means a scoped memory optimization is implemented and the tooling shows reduced peak usage for the documented ODD Pythia8 ttbar workloads.

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