To solve this optimization problem, we need to define the decision variables and the objective function. Let's denote the number of rail shipments as `R` and the number of truck shipments as `T`. The goal is to maximize the total amount of milk transported.

The total amount of milk transported by rail is `400 * R` litres, and the total amount of milk transported by truck is `200 * T` litres. Therefore, the objective function to be maximized is:

`Total Milk = 400R + 200T`

We have two constraints:
1. The budget constraint: The cost per rail shipment is $100, and the cost per truck shipment is $85. Given a budget of $3000, we can express this as:
   `100R + 85T <= 3000`
2. The constraint that the number of rail shipments cannot exceed the number of truck shipments:
   `R <= T`

Now, let's translate these constraints and the objective function into Gurobi code in Python.

```python
from gurobipy import *

# Create a new model
m = Model("Milk Transportation")

# Define decision variables
R = m.addVar(vtype=GRB.INTEGER, name="Rail Shipments")
T = m.addVar(vtype=GRB.INTEGER, name="Truck Shipments")

# Objective function: Maximize total milk transported
m.setObjective(400*R + 200*T, GRB.MAXIMIZE)

# Constraints
m.addConstr(100*R + 85*T <= 3000, "Budget Constraint")
m.addConstr(R <= T, "Rail vs Truck Constraint")

# Non-negativity constraints (since you can't have negative shipments)
m.addConstr(R >= 0, "Non-negative Rail Shipments")
m.addConstr(T >= 0, "Non-negative Truck Shipments")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution found: {R.varName} = {R.x}, {T.varName} = {T.x}")
    print(f"Total milk transported: {400*R.x + 200*T.x} litres")
else:
    print("No optimal solution found")

```