Here's the formulation of the linear program and the Gurobi code to solve it:

**Decision Variables:**

* `x`: Number of peanut butter smoothies made.
* `y`: Number of almond butter smoothies made.

**Objective Function:**

Maximize profit: `3x + 5y`

**Constraints:**

* Peanut butter constraint: `2x <= 80`
* Almond butter constraint: `3y <= 90`
* Milk constraint: `3x + 3y <= 100`
* Non-negativity constraints: `x >= 0`, `y >= 0`


```python
import gurobipy as gp

# Create a new model
m = gp.Model("smoothie_optimization")

# Create decision variables
x = m.addVar(vtype=gp.GRB.CONTINUOUS, name="x") # Peanut butter smoothies
y = m.addVar(vtype=gp.GRB.CONTINUOUS, name="y") # Almond butter smoothies

# Set objective function
m.setObjective(3*x + 5*y, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(2*x <= 80, "peanut_butter_constraint")
m.addConstr(3*y <= 90, "almond_butter_constraint")
m.addConstr(3*x + 3*y <= 100, "milk_constraint")

# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"Number of peanut butter smoothies (x): {x.x}")
    print(f"Number of almond butter smoothies (y): {y.x}")
    print(f"Maximum Profit: {m.objVal}")
elif m.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status {m.status}")

```
