## Step 1: Define the symbolic representation of the problem

Let's define the symbolic variables:
- $x_1$ represents the acres of coconut trees
- $x_2$ represents the acres of banana trees

The objective function to maximize profit is:
\[ \text{Maximize:} \quad 400x_1 + 350x_2 \]

The constraints are:
1. Budget constraint: $200x_1 + 150x_2 \leq 15000$
2. Labor constraint: $5x_1 + 4x_2 \leq 750$
3. Non-negativity constraint: $x_1 \geq 0, x_2 \geq 0$
4. Acres of land constraint: $x_1 + x_2 \leq 200$

## Step 2: Convert the problem into a Gurobi code

We will use the Gurobi library in Python to solve this linear programming problem.

```python
import gurobi as gp

# Create a new model
m = gp.Model("George's Land Optimization")

# Define the variables
x1 = m.addVar(name="coconut_trees", lb=0)  # Acres of coconut trees
x2 = m.addVar(name="banana_trees", lb=0)  # Acres of banana trees

# Objective function: Maximize profit
m.setObjective(400 * x1 + 350 * x2, gp.GRB.MAXIMIZE)

# Budget constraint
m.addConstr(200 * x1 + 150 * x2 <= 15000, name="budget_constraint")

# Labor constraint
m.addConstr(5 * x1 + 4 * x2 <= 750, name="labor_constraint")

# Acres of land constraint
m.addConstr(x1 + x2 <= 200, name="land_constraint")

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Acres of coconut trees: {x1.varValue}")
    print(f"Acres of banana trees: {x2.varValue}")
    print(f"Max Profit: {m.objVal}")
else:
    print("No optimal solution found.")
```

## Step 3: Provide the symbolic representation in JSON format

```json
{
    'sym_variables': [('x1', 'acres of coconut trees'), ('x2', 'acres of banana trees')],
    'objective_function': '400*x1 + 350*x2',
    'constraints': [
        '200*x1 + 150*x2 <= 15000',
        '5*x1 + 4*x2 <= 750',
        'x1 >= 0',
        'x2 >= 0',
        'x1 + x2 <= 200'
    ]
}
```

The final answer is: 

```python
import gurobi as gp

# Create a new model
m = gp.Model("George's Land Optimization")

# Define the variables
x1 = m.addVar(name="coconut_trees", lb=0)  # Acres of coconut trees
x2 = m.addVar(name="banana_trees", lb=0)  # Acres of banana trees

# Objective function: Maximize profit
m.setObjective(400 * x1 + 350 * x2, gp.GRB.MAXIMIZE)

# Budget constraint
m.addConstr(200 * x1 + 150 * x2 <= 15000, name="budget_constraint")

# Labor constraint
m.addConstr(5 * x1 + 4 * x2 <= 750, name="labor_constraint")

# Acres of land constraint
m.addConstr(x1 + x2 <= 200, name="land_constraint")

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Acres of coconut trees: {x1.varValue}")
    print(f"Acres of banana trees: {x2.varValue}")
    print(f"Max Profit: {m.objVal}")
else:
    print("No optimal solution found.")
```