To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the problem statement.

Let's define:
- $x_1$ as the number of rail shipments,
- $x_2$ as 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 and each truck shipment can take 200 litres, the objective function can be written as:
\[ \text{Maximize} \quad 400x_1 + 200x_2 \]

The constraints are as follows:
1. The cost per rail shipment is $100, and the cost per truck shipment is $85, with a total budget of $3000. This gives us the constraint:
\[ 100x_1 + 85x_2 \leq 3000 \]
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:
\[ x_1 \geq 0, \quad x_2 \geq 0 \]

And since we are dealing with shipments, which must be whole numbers, both $x_1$ and $x_2$ should be integers.

In symbolic notation, our problem can be represented as:
```json
{
  'sym_variables': [('x1', 'number of rail shipments'), ('x2', 'number of truck shipments')],
  'objective_function': '400*x1 + 200*x2',
  'constraints': ['100*x1 + 85*x2 <= 3000', 'x1 <= x2', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's implement this problem using Gurobi in Python:

```python
from gurobipy import *

# Create a new model
m = Model("Milk_Transport")

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="rail_shipments")
x2 = m.addVar(vtype=GRB.INTEGER, name="truck_shipments")

# Set objective function
m.setObjective(400*x1 + 200*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(100*x1 + 85*x2 <= 3000, "budget_constraint")
m.addConstr(x1 <= x2, "shipment_ratio_constraint")
m.addConstr(x1 >= 0, "non_neg_rail")
m.addConstr(x2 >= 0, "non_neg_truck")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Rail Shipments: {x1.x}")
    print(f"Truck Shipments: {x2.x}")
    print(f"Total Milk Transported: {400*x1.x + 200*x2.x} litres")
else:
    print("No optimal solution found.")
```