The "mismatched tokenization" error means that one or more "spans" you've added to your data do not map to the tokens assigned by the model's tokenizer. For example, let's say you have a sentence like "Next Monday~" and you've labelled the span for Monday – but the tokenizer only splits the text into two tokens: ["Next", "Monday~"]. Then your annotations will never be "true", because there will never be a token "Monday" in that example. If you're training a model with those examples, that would be pretty bad, because it wouldn't learn anything.
TL;DR: All "spans" should map to token boundaries. If you know what the tokenization should be (e.g. you already have the pre-tokenized text), you can also provide a "tokens" key in the data that tells Proidgy how the text should be split.
You can also use the method described here to find the examples with mismatched tokenization – maybe it turns out it's only one or two texts with very specific punctuation or extra whitespace.
That's expected. If you do not specify a --label on the command line, Prodigy will use the labels that are present in the model. If you're running ner.manual, you usually always want to specify one or more label options via the --label argument. For example, you could have your examples pre-labelled with DATE, and then manually add another label PERSON. In that case, you could write --label DATE,PERSON, and you'd be able to select those labels.