## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ = acres of corn
- $x_2$ = acres of wheat

## Step 2: Formulate the objective function
The profit per acre of corn is $200, and the profit per acre of wheat is $300. The objective function to maximize profit is:
\[ \text{Maximize:} \quad 200x_1 + 300x_2 \]

## 3: Define the constraints
1. Total land constraint: The farmer has 40 acres of land.
\[ x_1 + x_2 \leq 40 \]
2. Minimum corn constraint: He must grow at least 6 acres of corn.
\[ x_1 \geq 6 \]
3. Minimum wheat constraint: He must grow at least 12 acres of wheat.
\[ x_2 \geq 12 \]
4. Corn vs. wheat constraint: He can only grow at most 2 times the amount of corn as wheat.
\[ x_1 \leq 2x_2 \]

## 4: Symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'acres of corn'), ('x2', 'acres of wheat')],
'objective_function': '200*x1 + 300*x2',
'constraints': [
'x1 + x2 <= 40',
'x1 >= 6',
'x2 >= 12',
'x1 <= 2*x2'
]
}
```

## 5: Gurobi code to solve the problem
```python
import gurobipy as gp

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

# Define the variables
x1 = m.addVar(name="corn", lb=0)  # acres of corn
x2 = m.addVar(name="wheat", lb=0)  # acres of wheat

# Objective function: maximize profit
m.setObjective(200*x1 + 300*x2, gp.GRB.MAXIMIZE)

# Constraints
m.addConstr(x1 + x2 <= 40, name="total_land")
m.addConstr(x1 >= 6, name="min_corn")
m.addConstr(x2 >= 12, name="min_wheat")
m.addConstr(x1 <= 2*x2, name="corn_vs_wheat")

# Solve the model
m.optimize()

# Print the results
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal acres of corn: {x1.varValue}")
    print(f"Optimal acres of wheat: {x2.varValue}")
    print(f"Maximum profit: ${200*x1.varValue + 300*x2.varValue}")
else:
    print("The model is infeasible")
```