## Problem Description and Formulation

The problem is a classic example of a linear programming problem. The goal is to maximize the profit by deciding how many acres of mushrooms and truffles to grow, given the constraints on land, maintenance cost, and care hours.

Let's define the variables:

- \(x\): acres of mushrooms
- \(y\): acres of truffles

The objective function to maximize profit is:
\[ \text{Maximize:} \quad 200x + 500y \]

Subject to the constraints:
1. Land constraint: \( x + y \leq 90 \)
2. Maintenance cost constraint: \( 80x + 200y \leq 13500 \)
3. Care hours constraint: \( 2x + 3y \leq 120 \)
4. Non-negativity constraints: \( x \geq 0, y \geq 0 \)

## Gurobi Code

To solve this problem using Gurobi in Python, we will first install the Gurobi library if it's not already installed. You can install it using pip:

```bash
pip install gurobi
```

Now, let's write the Gurobi code:

```python
import gurobi as gp

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

# Define the variables
x = m.addVar(lb=0, name="mushrooms")  # acres of mushrooms
y = m.addVar(lb=0, name="truffles")  # acres of truffles

# Objective function: Maximize profit
m.setObjective(200*x + 500*y, gp.GRB.MAXIMIZE)

# Land constraint
m.addConstr(x + y <= 90, name="land_constraint")

# Maintenance cost constraint
m.addConstr(80*x + 200*y <= 13500, name="maintenance_cost_constraint")

# Care hours constraint
m.addConstr(2*x + 3*y <= 120, name="care_hours_constraint")

# Optimize the model
m.optimize()

# Print the results
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal acres of mushrooms: {x.varValue}")
    print(f"Optimal acres of truffles: {y.varValue}")
    print(f"Maximal profit: {m.objVal}")
else:
    print("The model is infeasible")
```