Add `export_fault_lines()` one liner export

[mferrera]

Currently fault polygons are exported from RMS projects in the following way:

• Specify the horizon names
• Specify a folder, under the assumption this folder exists for each horizon
• Loop through each horizon, and if that folder is there and not empty,
  • Create an xtgeo.Polygon from the RMS polygon
  • Create an ExportData instance for each file format being exported
  • Set the polygon fileformat
  • Export

Simplification

A possible one-liner export could instead look something like this:

for hname in HORIZON_NAMES:
    for fmt in ("csv", "irap_ascii"):
        export_fault_polygons(
            project: Any, 
            folder=POLYGON_FOLDER,
            vertical_domain="depth",  # or "time"
            fformat=fmt,
            workflow="RMS Structural Model",  # Can be set to something default instead?
            # classification=,  ? needed ?
            # cfg=CFG,  ? needed ?
        )

Unanswered questions

• Do we need to give the global variables config, or can it be inferred?
  • In the volumetrics export, it is defaulted
  • https://github.com/equinor/fmu-dataio/blob/2e58bdf55eef150ad9c6ce09dfbd4ebf540fd8bd/src/fmu/dataio/export/rms/volumetrics.py#L57
• Does classification need to be specified here?
• Can either of the outer loops be rolled into the function signature, or does that become too complex?
• Is this name of the function appropriate?
• How does this function signature align with the existing volumetrics one?
  • https://github.com/equinor/fmu-dataio/blob/2e58bdf55eef150ad9c6ce09dfbd4ebf540fd8bd/src/fmu/dataio/export/rms/volumetrics.py#L194-L205
• Is this scope of this function (fault polygons) too broad, or too narrow?
• How do Webviz/REP query and/or use fault polygons, if they do?

Tasks

• [ ] Implement the function
• [ ] Update documentation and examples
  • [ ] https://fmu-dataio.readthedocs.io/en/latest/examples.html#exporting-fault-polygons

[mferrera]

Consider naming this fault_lines over fault_polygons to prefer the data product over the data type.