Newer versions fail to include pdf table cells that are successfully handled in older versions

### Bug
The docling versions 2.13.0 onwards fail to include PDF table text cells that are well captured by version 2.12.0.

### Steps to reproduce
```
pipeline_options = PdfPipelineOptions()
pipeline_options.images_scale = IMAGE_RESOLUTION_SCALE
pipeline_options.generate_page_images = True
pipeline_options.generate_table_images = True
pipeline_options.generate_picture_images = True
pipeline_options.do_table_structure = True
pipeline_options.table_structure_options.do_cell_matching = True
pipeline_options.table_structure_options.mode = TableFormerMode.ACCURATE

doc_converter = DocumentConverter(
allowed_formats=[
InputFormat.PDF,
],
format_options={
InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options,
backend=PyPdfiumDocumentBackend),
}
)
```
Sorry I cannot provide the pdf file I used as it was an internal one and I was unable to make up one. But I include the data objects below.

### Docling version
docling>=2.13.0

### Python version
3.11, 3.10

### Naive ways I tried
The issue didn't come from the ML model (`ds4sd/docling-models`) because the versions share it, but comes from the change of script in `layout_model.py`, which calls the new utility class in `layout_postprocessor.py`. Here is the related commit: https://github.com/DS4SD/docling/commit/60dc852f16dc1adbb5e9284c81a146043a301ec1.

Changing the scores in `CONFIDENCE_THRESHOLDS` of `layout_postprocessor`.py to the original configurations of version` 2.12.0 `has no effect.

The code flow: `layout_model.py -> standard_pdf_pipeline (TableStructureModel) -> layout_predictor.py -> table_structure_model.py `(line 200). This produces the table_cluster object.
Here is part of the object:
- Texts that were captured in export_to_markdown():
```
Cluster(id=63, label=, bbox=BoundingBox(l=91.57151794433594, t=156.7459716796875, r=298.26287841796875, b=162.767578125, coord_origin=), confidence=1.0, cells=[Cell(id=43, text='ABC', bbox=BoundingBox(l=91.57151794433594, t=156.7459716796875, r=298.26287841796875, b=162.767578125, coord_origin=))], children=[]),
Cluster(id=19, label=, bbox=BoundingBox(l=91.27823638916016, t=165.1458740234375, r=327.4078674316406, b=187.966552734375, coord_origin=), confidence=0.6519731283187866, cells=[Cell(id=44, text=‘EFG’, bbox=BoundingBox(l=91.30944061279297, t=165.1458740234375, r=327.4078674316406, b=171.16741943359375, coord_origin=))
```

- Texts that failed to be captured:
```
Cluster(id=33, label=, bbox=BoundingBox(l=91.27202606201172, t=466.488525390625, r=319.7994384765625, b=489.31439208984375, coord_origin=), confidence=0.5935887098312378, cells=[Cell(id=69, text=‘111, bbox=BoundingBox(l=91.27202606201172, t=466.488525390625, r=186.20797729492188, b=472.4788818359375, coord_origin=)), Cell(id=70, text=‘222 ', bbox=BoundingBox(l=91.80242919921875, t=483.28656005859375, r=319.7994384765625, b=489.31439208984375, coord_origin=))], children=[]), Cluster(id=52, label=, bbox=BoundingBox(l=91.51538848876953, t=491.68560791015625, r=323.0069274902344, b=497.71343994140625, coord_origin=), confidence=0.4870221018791199, cells=[Cell(id=71, text=‘333 ', bbox=BoundingBox(l=91.51538848876953, t=491.68560791015625, r=323.0069274902344, b=497.71343994140625, coord_origin=))], children=[]), Cluster(id=24, label=, bbox=BoundingBox(l=91.1409912109375, t=500.05340576171875, r=324.97491455078125, b=539.72412109375, coord_origin=), confidence=0.6086541414260864, cells=[Cell(id=72, text='o4444’, bbox=BoundingBox(l=91.27826690673828, t=500.05340576171875, r=305.6815490722656, b=506.11248779296875, coord_origin=)), Cell(id=73, text=‘555 ', bbox=BoundingBox(l=91.27202606201172, t=508.48370361328125, r=324.43212890625, b=514.5115356445312, coord_origin=)), Cell(id=74, text='t555’, bbox=BoundingBox(l=91.1409912109375, t=516.8515014648438, r=281.83172607421875, b=522.8731079101562, coord_origin=)), Cell(id=75, text=‘6666’, bbox=BoundingBox(l=91.45295715332031, t=525.2659912109375, r=324.80145263671875, b=531.28759765625, coord_origin=))]
```