## Symbolic Representation

To solve the given problem, we first need to convert the natural language description into a symbolic representation.

Let's define the symbolic variables:
- $x_1$ represents the number of rail shipments.
- $x_2$ represents the number of truck shipments.

The objective is to maximize the total litres of milk transported. Given that each rail shipment can take 400 litres of milk and each truck shipment can take 200 litres of milk, the objective function can be represented as:

Maximize $400x_1 + 200x_2$

The constraints given are:
1. The cost per rail shipment is $100 and the cost per truck shipment is $85, with a total budget of $3000:
   $100x_1 + 85x_2 \leq 3000$

2. The number of rail shipments cannot exceed the number of truck shipments:
   $x_1 \leq x_2$

3. Non-negativity constraints, as the number of shipments cannot be negative:
   $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'rail shipments'), ('x2', 'truck shipments')],
    'objective_function': '400*x1 + 200*x2',
    'constraints': [
        '100*x1 + 85*x2 <= 3000',
        'x1 <= x2',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

def solve_milk_transportation_problem():
    # Create a new model
    model = gp.Model("MilkTransportation")

    # Define variables
    x1 = model.addVar(name="rail_shipments", vtype=gp.GRB.INTEGER, lb=0)
    x2 = model.addVar(name="truck_shipments", vtype=gp.GRB.INTEGER, lb=0)

    # Objective function: Maximize 400*x1 + 200*x2
    model.setObjective(400*x1 + 200*x2, gp.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(100*x1 + 85*x2 <= 3000, name="budget_constraint")
    model.addConstr(x1 <= x2, name="rail_truck_constraint")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gp.GRB.OPTIMAL:
        print(f"Optimal solution: rail shipments = {x1.varValue}, truck shipments = {x2.varValue}")
    else:
        print("No optimal solution found.")

solve_milk_transportation_problem()
```