To solve the optimization problem described, we first need to identify the decision variables, objective function, and constraints.

- Decision Variables:
  - \(x\): The number of acres allocated to almonds.
  - \(y\): The number of acres allocated to pecans.

- Objective Function:
  - Maximize net revenue: \(500x + 600y\).

- Constraints:
  - Land constraint: \(x + y \leq 80\) (since the farmer has 80 acres).
  - Labor constraint: \(1.5x + 3y \leq 275\) (since each acre of almonds requires 1.5 days and each acre of pecans requires 3 days, with a total of 275 days available).
  - Maintenance cost constraint: \(200x + 250y \leq 10000\) (since the maintenance costs per acre for almonds is $200 and for pecans is $250, with a budget of $10,000).

Given these components, we can formulate the problem in Gurobi using Python as follows:

```python
from gurobipy import *

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

# Define decision variables
x = m.addVar(vtype=GRB.CONTINUOUS, name="almonds_acres")
y = m.addVar(vtype=GRB.CONTINUOUS, name="pecans_acres")

# Objective function: Maximize net revenue
m.setObjective(500*x + 600*y, GRB.MAXIMIZE)

# Constraints
m.addConstr(x + y <= 80, "land_constraint")  # Land constraint
m.addConstr(1.5*x + 3*y <= 275, "labor_constraint")  # Labor constraint
m.addConstr(200*x + 250*y <= 10000, "maintenance_cost_constraint")  # Maintenance cost constraint

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Almonds acres: {x.x}")
    print(f"Pecans acres: {y.x}")
    print(f"Max net revenue: ${500*x.x + 600*y.x:.2f}")
else:
    print("No optimal solution found")
```