## Problem Description and Formulation

The farmer has 40 acres of land to grow corn and wheat. The goal is to maximize profit given the following constraints:

1. The farmer must grow at least 6 acres of corn.
2. The farmer must grow at least 12 acres of wheat.
3. The farmer can grow at most 2 times the amount of corn as wheat.
4. The total land used for corn and wheat cannot exceed 40 acres.
5. The profit per acre of corn is $200, and the profit per acre of wheat is $300.

Let's denote:
- \(C\) as the acres of corn,
- \(W\) as the acres of wheat.

The objective function to maximize profit (\(P\)) is:
\[ P = 200C + 300W \]

Subject to:
1. \( C \geq 6 \)
2. \( W \geq 12 \)
3. \( C \leq 2W \)
4. \( C + W \leq 40 \)
5. \( C, W \geq 0 \) (Implicitly assumed, as acres cannot be negative)

## Gurobi Code Formulation

We will now translate this problem into Gurobi code in Python:

```python
import gurobi

def solve_farmer_problem():
    # Create a new model
    m = gurobi.Model()

    # Define variables
    C = m.addVar(name="corn", lb=0)  # Acres of corn
    W = m.addVar(name="wheat", lb=0)  # Acres of wheat

    # Objective function: Maximize profit
    m.setObjective(200*C + 300*W, gurobi.GRB.MAXIMIZE)

    # Constraints
    m.addConstr(C >= 6, name="min_corn")
    m.addConstr(W >= 12, name="min_wheat")
    m.addConstr(C <= 2*W, name="corn_to_wheat_ratio")
    m.addConstr(C + W <= 40, name="total_land")

    # Solve the problem
    m.optimize()

    # Print solution
    if m.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal acres of corn: {C.varValue}")
        print(f"Optimal acres of wheat: {W.varValue}")
        print(f"Maximum profit: ${200*C.varValue + 300*W.varValue}")
    else:
        print("The problem is infeasible")

solve_farmer_problem()
```

This code sets up the optimization problem as described, solves it using Gurobi, and prints out the optimal acres of corn and wheat to grow in order to maximize profit, along with the maximum achievable profit. If the problem is infeasible (i.e., no solution satisfies all constraints), it will indicate that instead.