## Symbolic Representation

The problem can be converted into a symbolic representation as follows:

Let's denote:
- `x1` as acres of almonds
- `x2` as acres of pecans

The objective function to maximize net revenue is:
\[ 500x1 + 600x2 \]

The constraints are:
1. Land constraint: \[ x1 + x2 \leq 80 \]
2. Labor constraint: \[ 1.5x1 + 3x2 \leq 275 \]
3. Maintenance cost constraint: \[ 200x1 + 250x2 \leq 10000 \]
4. Non-negativity constraints: \[ x1 \geq 0, x2 \geq 0 \]

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'acres of almonds'), ('x2', 'acres of pecans')],
    'objective_function': '500*x1 + 600*x2',
    'constraints': [
        'x1 + x2 <= 80',
        '1.5*x1 + 3*x2 <= 275',
        '200*x1 + 250*x2 <= 10000',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define variables
x1 = model.addVar(name="almonds", lb=0)  # acres of almonds
x2 = model.addVar(name="pecans", lb=0)   # acres of pecans

# Objective function: maximize net revenue
model.setObjective(500*x1 + 600*x2, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(x1 + x2 <= 80, name="land_constraint")
model.addConstr(1.5*x1 + 3*x2 <= 275, name="labor_constraint")
model.addConstr(200*x1 + 250*x2 <= 10000, name="maintenance_cost_constraint")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal acres of almonds: {x1.varValue}")
    print(f"Optimal acres of pecans: {x2.varValue}")
    print(f"Maximal net revenue: {model.objVal}")
else:
    print("The model is infeasible.")
```