You might want to look closely at
What are the consequences of not meeting the assumptions for the residuals of ARIMA model? as it discusses the FLAW of using ADF for discrete data ( like small count daily data)
If your values are "large" thus approaching continuous data which can often exhibit some of the following effects and the idea is to identify the important ones .....
Trends, Seasonality, Monthly or Weekly patterns, Level Shifts, Big increases and drops, but not necessarily a trend, Autoregressive behavior (ARIMA), Fixed Day of the month, Seasonal Pulses (Changes in Day of the week effects) , Interventions, Holidays plus before and after and others like day-of-the-month , week-of-the-month. Error variance and/or parameter variance over time can also come into play AND possible response to known external effects .
On average differencing daily data is a very bad idea (although possible) given my long experience with analyzing daily data based upon my consultancy and a number of SE posts on this subject. Search on "user:3382 daily " for a number of comments about daily data
Additionally taking/imposing/assuming the need for a tranformation like logs is very dangerous stuff . See When (and why) should you take the log of a distribution (of numbers)? for when and why to take logs.