I don't feel comfortable enough to comment on your autocorrelated errors issue (nor about the different implementations in lme4 vs. nlme), but I can speak to the rest.

Your model `m1` is a random-intercept model, where you have included the cross-level interaction between Treatment and Day (the effect of Day is allowed to vary between Treatment groups). In order to allow for the change over time to differ across participants (i.e. to explicitly model individual differences in change over time), you also need to allow for the effect of Day to be *random*. To do this, you would specify:

    m2 <- lmer(Obs ~ Day + Treatment + Day:Treatment + (Day | Subject), mydata)

In this model: 

 - The intercept if the predicted score for the treatment reference category at Day=0
 - The coefficient for Day is the predicted change over time for each 1-unit increase in days for the treatment reference category
 - The coefficients for the two dummy codes for the treatment groups (automatically created by R) are the predicted difference between each remaining treatment group and the reference category at Day=0
 - The coefficients for the two interaction terms are the difference in the effect of time (Day) on predicted scores between the reference category and the remaining treatment groups. 

Both the intercepts and the effect of Day on score are random (each subject is allowed to have a different predicted score at Day=0 and a different linear change over time). The covariance between intercepts and slopes is also being modeled (they are allowed to covary).

As you can see, the interpretation of the coefficients for the two dummy variables are conditional on Day=0. They will tell you if the predicted score at Day=0 for the reference category is significantly different from the two remaining treatment groups. Therefore, where you decide to center your Day variable is important. If you center at Day 1, then the coefficients tell you whether the predicted score for the reference category **at Day 1** is significantly different from the predicted score of the two remaining groups. This way, you could see if there are **pre-existing differences between the groups**. If you center at Day 3, then the coefficients tell you whether the predicted score for the reference category **at Day 3** is significantly different from the predicted score of the two remaining groups. This way, you could see if there are **differences between the groups at the end of the intervention**.

Finally, note that Subjects are *not* nested within Treatment. Your three treatments are not random levels of a population of levels to which you want to generalize your results--rather, as you mentioned, your levels are fixed, and you want to generalize your results to these levels only. (Not to mention, you shouldn't use multilevel modeling if you have only 3 upper-level units; see Maas & Hox, 2005.) Instead, treatment is a level-2 predictor, i.e. a predictor which takes a single value across Days (level-1 units) for each subject. Therefore, it is merely included as a predictor in your model.


Reference:
<br/>Maas, C. J. M., & Hox, J. J. (2005). Sufficient sample sizes for multilevel modeling. <i>Methodology: European Journal of Research Methods for the Behavioral and Social Sciences</i>, <i>1</i>, 86-92.