To solve this optimization problem, we first need to translate the natural language description into a symbolic representation. Let's define the variables as follows:

- $x_1$: The number of acres of almonds grown.
- $x_2$: The number of acres of pecans grown.

The objective function is to maximize the net revenue, which can be represented algebraically as:
\[500x_1 + 600x_2\]

The constraints are based on the available labor and maintenance costs. For labor, the constraint is:
\[1.5x_1 + 3x_2 \leq 275\]
And for maintenance costs, it's:
\[200x_1 + 250x_2 \leq 10000\]

Additionally, we have non-negativity constraints since the number of acres cannot be negative:
\[x_1 \geq 0\]
\[x_2 \geq 0\]

And because the total land is 80 acres, we also have:
\[x_1 + x_2 \leq 80\]

Now, let's represent this problem in the requested format:

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

Next, we'll translate this problem into Gurobi code in Python:

```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="acres_of_almonds")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="acres_of_pecans")

# Set the objective function
m.setObjective(500*x1 + 600*x2, GRB.MAXIMIZE)

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

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Acres of almonds: {x1.x}")
    print(f"Acres of pecans: {x2.x}")
    print(f"Maximum net revenue: {m.objVal}")
else:
    print("No optimal solution found")
```