SpanCat - Custom SpanCat NER model with whitespace issues

## How to reproduce the behaviour

This is going to be difficult to reproduce as the behaviour I am seeing it during inference with a custom trained model. I have trained a multilingual Span Categorizer NER model on a combination of publicly available datasets, I have pasted in the config I used for training below. I used SpaCy's convert module to convert the IOB2 text files to SpaCy format to create the train, dev and test sets.

When testing the model on new data I am observing two issues around whitespace:
1. In some cases preceding whitespace is included in the detected entity span
2. More significantly I am seeing a sensitivity to entity detection based on if there is preceding multi-whitespace For example:
```
import spacy
model_path = ""
spacy_model = spacy.load(model_path)

# single space
output = spacy_model ("4:30 Jerry Smith")
print(f"Output with a single space: {output.spans['ents']}")
# two spaces
output = spacy_model ("4:30 Jerry Smith")
print(f"Output with two spaces: {output.spans['ents']}")
# three spaces
output = spacy_model ("4:30 Jerry Smith")
print(f"Output with three spaces: {output.spans['ents']}")
# four spaces
output = spacy_model ("4:30 Jerry Smith")
print(f"Output with four spaces: {output.spans['ents']}")
# five spaces
output = spacy_model ("4:30 Jerry Smith")
print(f"Output with five spaces: {output.spans['ents']}")
# six spaces
output = spacy_model ("4:30 Jerry Smith")
print(f"Output with six spaces: {output.spans['ents']}")
```
The above outputs:
```
Output with a single space: [Jerry Smith]
Output with two spaces: [Jerry Smith]
Output with three spaces: []
Output with four spaces: [Jerry Smith]
Output with five spaces: [ Jerry Smith]
Output with six spaces: []
```

I understand these are short sentences with little context, but I wouldn't expect this sensitivity to preceding whitespace. Just wondering if this has been seen before and if there is anything that can be done during model training to avoid it.

## Your Environment

* Operating System:
- **spaCy version:** 3.8.6
- **Platform:** Linux-6.6.87.1-microsoft-standard-WSL2-x86_64-with-glibc2.36
- **Python version:** 3.12.10

## Config
```
[paths]
train = null
dev = null
vectors = null
init_tok2vec = null

[system]
gpu_allocator = null
seed = 0

[nlp]
lang = "xx"
pipeline = ["spancat"]
disabled = []
before_creation = null
after_creation = null
after_pipeline_creation = null
batch_size = 512
tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}

[components]

[components.spancat]
factory = "spancat"
max_positive = null
spans_key = "ents"
threshold = 0.5

[components.spancat.model]
@architectures = "spacy.SpanCategorizer.v1"

[components.spancat.model.reducer]
@layers = "spacy.mean_max_reducer.v1"
hidden_size = 128

[components.spancat.model.scorer]
@layers = "spacy.LinearLogistic.v1"
nO = null
nI = null

[components.spancat.model.tok2vec]
@architectures = "spacy.Tok2Vec.v2"

[components.spancat.model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = ${components.spancat.model.tok2vec.encode:width}
rows = [5000,2000,1000,1000]
attrs = ["ORTH","PREFIX","SUFFIX","SHAPE"]
include_static_vectors = false

[components.spancat.model.tok2vec.encode]
@architectures = "spacy.MaxoutWindowEncoder.v2"
width = 96
depth = 4
window_size = 1
maxout_pieces = 3

[components.spancat.suggester]
@misc = "spacy.ngram_range_suggester.v1"
min_size = 1
max_size = 5

[corpora]

[corpora.dev]
@readers = "spacy.Corpus.v1"
path = ${paths.dev}
gold_preproc = false
max_length = 0
limit = 0
augmenter = null

[corpora.train]
@readers = "spacy.Corpus.v1"
path = ${paths.train}
gold_preproc = false
max_length = 0
limit = 0
augmenter = null

[training]
train_corpus = "corpora.train"
dev_corpus = "corpora.dev"
seed = ${system:seed}
gpu_allocator = ${system:gpu_allocator}
dropout = 0.1
accumulate_gradient = 1
patience = 20000
max_epochs = 0
max_steps = 0
eval_frequency = 10000
frozen_components = []
before_to_disk = null
annotating_components = []

[training.batcher]
@batchers = "spacy.batch_by_words.v1"
discard_oversize = false
tolerance = 0.2
get_length = null

[training.batcher.size]
@schedules = "compounding.v1"
start = 100
stop = 1000
compound = 1.001
t = 0.0

[training.logger]
@loggers = "spacy.ConsoleLogger.v1"
progress_bar = false

[training.optimizer]
@optimizers = "Adam.v1"
beta1 = 0.9
beta2 = 0.999
L2_is_weight_decay = true
L2 = 0.01
grad_clip = 1.0
use_averages = true
eps = 0.00000001
learn_rate = 0.001

[training.score_weights]
spans_ents_f = 1.0
spans_ents_p = 0.0
spans_ents_r = 0.0
spans_sc_f = null
spans_sc_p = null
spans_sc_r = null

[pretraining]

[initialize]
vocab_data = null
vectors = null
init_tok2vec = ${paths.init_tok2vec}
before_init = null
after_init = null
lookups = null

[initialize.components]

[initialize.tokenizer]
```