Adding energy and water budget diagnostics (for evaluation of ML & physical models)

### What problem does your feature request solve?
New diagnostics for global time series of energy (kinetic, potential, internal) budgets and exchanges, and moisture budgets, to evaluate the fidelity of these in ML (and physical models)

### Describe the solution you'd like
Global and user specified regional integrals of energy and moisture components and energy exchanges as time series for ML and physical models (deterministic and ensemble)

This would involve back end common implementations for the lat-lon grid of:
- full domain integrals at each level
- gradient and divergent operators at each level
- vertical integrals and derivatives on pressure levels

And front end implementations where the above functionality is applied to compute budgets and exchanges on datasets for different sources (ie: AIFS, ERA5, IFS, LFRic, etc) over specified domains, time periods and ensemble sizes

### Describe alternatives you've considered
Computing these diagnostics directly on the ML model grid (as an unstructured Delaunay/Voronoi mesh), rather than having these re-mapped to the lat-lon grid (possible future extension).

Bespoke implementation of diagnostics for the ML Hackathon:
https://github.com/JorgeBornemann/Momentum_ML_Hackathon_2025/tree/energy_diagnostics/theme_2/energy_diagnostics

Related Scores Github issue: https://github.com/nci/scores/issues/995

Start by reviewing the bespoke energy_diagnostics implementation linked in the issue and the related Scores issue #995. Define the scope for shared lat-lon operators, vertical integrations, and regional or global energy and moisture time series across the listed model sources; completion should cover both backend calculations and frontend dataset application.

Written by the indexing model from the issue text.