allow flexible convert_dates parameter in pandas.read_json
Currently, when using pandas.read_json to convert a json structure into a DataFrame, it is difficult to parse on the fly datetime except with convert_dates that allows rules:
```
convert_dates : boolean, default True
List of columns to parse for dates; If True, then try to parse datelike columns default is True; a column label is datelike if
it ends with '_at',
it ends with '_time',
it begins with 'timestamp',
it is 'modified', or
it is 'date'
```

It would be more flexible to allow to specify either a boolean (and keep the current rules) or to specify some (simplified) list of regexp pattern like: 
- to replicate current rules: `convert_dates=("*_at","*_time","timestamp*","modified","date")`
- to allow alternatives:`convert_dates=("*_at","*_on","datetime_*","start","end")`

Haven't yet looked at the implementation, just sharing a feature request...