To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints using these variables.

Let's denote:
- \(x_1\) as the number of acres for growing mushrooms,
- \(x_2\) 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. Therefore, the objective function can be represented as:
\[ \text{Maximize:} \quad 200x_1 + 500x_2 \]

The constraints are based on the maintenance cost and the care time available:
1. Maintenance Cost Constraint: Each acre of mushrooms requires $80 in maintenance, and each acre of truffles requires $200. The total budget for maintenance is $13,500.
\[ 80x_1 + 200x_2 \leq 13500 \]
2. Care Time Constraint: Each acre of mushrooms requires 2 hours of care, and each acre of truffles requires 3 hours. The total available time for care is 120 hours.
\[ 2x_1 + 3x_2 \leq 120 \]
3. Non-Negativity Constraints: Since the number of acres cannot be negative, we also have:
\[ x_1 \geq 0 \]
\[ x_2 \geq 0 \]
4. Total Land Constraint: The total land available is 90 acres.
\[ x_1 + x_2 \leq 90 \]

Now, let's represent the problem symbolically as requested:

```json
{
  'sym_variables': [('x1', 'acres of mushrooms'), ('x2', 'acres of truffles')],
  'objective_function': '200*x1 + 500*x2',
  'constraints': [
    '80*x1 + 200*x2 <= 13500',
    '2*x1 + 3*x2 <= 120',
    'x1 >= 0',
    'x2 >= 0',
    'x1 + x2 <= 90'
  ]
}
```

To solve this problem using Gurobi in Python, we'll use the following code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, name="acres_of_mushrooms")
x2 = m.addVar(lb=0, name="acres_of_truffles")

# Set the objective function
m.setObjective(200*x1 + 500*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(80*x1 + 200*x2 <= 13500, "maintenance_cost")
m.addConstr(2*x1 + 3*x2 <= 120, "care_time")
m.addConstr(x1 + x2 <= 90, "total_land")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Acres of Mushrooms: {x1.x}")
    print(f"Acres of Truffles: {x2.x}")
    print(f"Maximum Profit: ${200*x1.x + 500*x2.x:.2f}")
else:
    print("No optimal solution found")
```