A few things here. Assuming your utilities are correct, the fraction would be the reciprocal of yours (price in the numerator) so that you have euro per km.
Then you want to use the the number of km in one utility point as your denominator, NOT, as you have, the utility of the most appealing distance.
Ideally you would do this at the respondent level, not the aggregate, so you'll want to run a hierarchical Bayesian logit utility model (or a mixed logit) so that you can run this formula for each respondent. And then you probably want to look at the median (not the mean) of this respondent-level measure.
Finally, for this to work well, we need to know the amount of utility peer km. Unfortunately, you seem to have measured your distance as ranges (level 1 and 2) and as an open-ended range (level 3). So is the utility of 0,5795 for 0 km or 30 or 50? We don 't know. And what is the number of km measured by 150+? Is it 150 km or 200 or is it 384,400 km, the distance to the moon? Again, we don't know. Given this inability to tie specific distances to your distance levels, I think your data is not well-suited to computing a WTP.
Alternatively, you could look at the amount of euros it would take to equalize the difference between 0-50 km and 150km+, which is 0,5794 - (-0,6129) or 1.1923. That euro difference is the equalization price, another measure of WTP. Again, doing this at the respondent level and using the median is probably your best bet. This gives you the amount of euros the median respondent would pay to compensate them for the difference in your maximum and minimum distances (but it will not directly give you a euros per km metric).