## Symbolic Representation

The problem can be converted into a symbolic representation as follows:

- **Variables:**
  - $x_1$ = acres of apple trees
  - $x_2$ = acres of peach trees

- **Objective Function:** Maximize $900x_1 + 1100x_2$

- **Constraints:**
  1. $x_1 + x_2 \leq 400$ (total acres of land)
  2. $x_1 \geq 60$ (minimum acres of apple trees)
  3. $x_2 \geq 40$ (minimum acres of peach trees)
  4. $x_2 \leq 3x_1$ (at most 3 times the amount of peach trees as apple trees)
  5. $x_1, x_2 \geq 0$ (non-negativity constraint, though implicitly satisfied by constraints 2 and 3)

## JSON Representation

```json
{
  'sym_variables': [('x1', 'acres of apple trees'), ('x2', 'acres of peach trees')],
  'objective_function': '900*x1 + 1100*x2',
  'constraints': [
    'x1 + x2 <= 400',
    'x1 >= 60',
    'x2 >= 40',
    'x2 <= 3*x1'
  ]
}
```

## Gurobi Code

```python
import gurobipy as gp

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

# Define variables
x1 = m.addVar(name="apple_trees", lb=0)  # acres of apple trees
x2 = m.addVar(name="peach_trees", lb=0)  # acres of peach trees

# Objective function: Maximize profit
m.setObjective(900*x1 + 1100*x2, gp.GRB.MAXIMIZE)

# Constraints
m.addConstr(x1 + x2 <= 400, name="total_land")  # Total land constraint
m.addConstr(x1 >= 60, name="min_apple_trees")  # Minimum apple trees
m.addConstr(x2 >= 40, name="min_peach_trees")  # Minimum peach trees
m.addConstr(x2 <= 3*x1, name="peach_to_apple_ratio")  # Peach to apple ratio

# Solve the model
m.optimize()

# Print solution
if m.status == gp.GRB.OPTIMAL:
  print(f"Optimal solution: Acres of apple trees = {x1.varValue}, Acres of peach trees = {x2.varValue}")
  print(f"Maximum profit: ${900*x1.varValue + 1100*x2.varValue}")
else:
  print("No optimal solution found")
```