## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the acres of mushrooms
- $x_2$ represents the acres of truffles

## Step 2: Translate the objective function into symbolic notation
The profit per acre of mushrooms is $200, and the profit per acre of truffles is $500. The objective function to maximize profit is:
\[ \text{Maximize:} \quad 200x_1 + 500x_2 \]

## 3: Translate the constraints into symbolic notation
The farmer has:
- 90 acres of land: $x_1 + x_2 \leq 90$
- At most $13500 to spend on maintenance: $80x_1 + 200x_2 \leq 13500$
- 120 hours of time available for care: $2x_1 + 3x_2 \leq 120$
- Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## 4: Provide the symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'acres of mushrooms'), ('x2', 'acres of truffles')],
'objective_function': '200*x1 + 500*x2',
'constraints': [
    'x1 + x2 <= 90',
    '80*x1 + 200*x2 <= 13500',
    '2*x1 + 3*x2 <= 120',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 5: Write the Gurobi code to solve the problem
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(lb=0, name="mushrooms")
    x2 = model.addVar(lb=0, name="truffles")

    # Objective function
    model.setObjective(200*x1 + 500*x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(x1 + x2 <= 90, name="land_constraint")
    model.addConstr(80*x1 + 200*x2 <= 13500, name="maintenance_constraint")
    model.addConstr(2*x1 + 3*x2 <= 120, name="care_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Acres of mushrooms: {x1.varValue}")
        print(f"Acres of truffles: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```