Just to make sure I understand the problem correctly: You have some annotated examples that weren't yet annotated with label 8. You have patterns for label 8, and you want to add those pattern matches to the examples that are already in your dataset.
If you run a recipe like ner.manual with patterns and examples with pre-defined "spans", those spans will be overwritten. That's expected – otherwise, the results would be pretty confusing, you'd constantly have to resolve overlaps between existing spans and matches etc.
However, if you have a way to identify the examples with unannotated matches, you can re-annotate only those, or go over them again to only add label 8. One approach would be to use spaCy's Matcher or PhraseMatcher directly, add your patterns for label 8, loop over the examples in your dataset and match on each text. This lets you extract all annotated examples that contain matches for label 8. You can then queue them up again for annotation.
If you're training your model the "regular" way and under the assumption that all unannotated tokens are not entities, this would be a problem, yes. The impact on accuracy may not be huge for 30 examples out of thousands, but it can lead to worse results.
Essentially, the model will try to "make sense of" the fact that one a word is labelled in one context, and not labelled in a different similar context. The weights you train will reflect that – but if they're based on incorrect labels, the may lead to worse results. Similarly, if some of those 30 examples end up in your evaluation set, your evaluation will be wrong, because you're validating your model against incorrect answers (e.g. penalising it if if predicts label 8 in one of those texts). This means your accuracy will be unreliable.