To solve this optimization problem, we need to define the decision variables, the objective function, and the constraints. 

Let's denote:
- $M$ as the number of acres for growing mushrooms,
- $T$ as the number of acres for growing truffles.

The objective is to maximize profit. The profit per acre from mushrooms is $200, and from truffles is $500. So, the total profit can be represented as $200M + 500T$.

There are two main constraints:
1. **Maintenance Cost Constraint**: The farmer has at most $13,500 to spend on maintenance. Since each acre of mushrooms requires $80 in maintenance and each acre of truffles requires $200 in maintenance, the total cost constraint can be represented as $80M + 200T \leq 13500$.
2. **Care Time Constraint**: The farmer has 120 hours available for care. With mushrooms requiring 2 hours per acre and truffles requiring 3 hours per acre, the time constraint can be represented as $2M + 3T \leq 120$.

Additionally, we have non-negativity constraints since the number of acres cannot be negative: $M \geq 0$ and $T \geq 0$.

We also know that the total land available is 90 acres, which gives us another constraint: $M + T \leq 90$.

Here's how we can represent this problem in Gurobi using Python:

```python
from gurobipy import *

# Create a new model
m = Model("Farm_Optimization")

# Define the decision variables
M = m.addVar(vtype=GRB.CONTINUOUS, name="Mushrooms_Acres")
T = m.addVar(vtype=GRB.CONTINUOUS, name="Truffles_Acres")

# Objective function: Maximize profit
m.setObjective(200*M + 500*T, GRB.MAXIMIZE)

# Constraints
m.addConstr(80*M + 200*T <= 13500, "Maintenance_Cost")
m.addConstr(2*M + 3*T <= 120, "Care_Time")
m.addConstr(M + T <= 90, "Total_Land")
m.addConstr(M >= 0, "Non_Negativity_Mushrooms")
m.addConstr(T >= 0, "Non_Negativity_Truffles")

# Optimize the model
m.optimize()

# Print the results
for v in m.getVars():
    print(f"{v.varName}: {v.x}")
print(f"Objective (Max Profit): {m.objVal}")
```