To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation that can be solved using linear programming techniques. The goal is to maximize savings (or profit) by deciding how many acres of solar panels and windmills should be built given the constraints on land and resources.

Let's denote:
- \(x\) as the number of acres of solar panels,
- \(y\) as the number of acres of windmills,
- The total available land is 120 acres, so we have \(x + y \leq 120\),
- The resource constraint is given by \(20x + 40y \leq 2000\), since each acre of solar panels requires 20 units of resources and each acre of windmills requires 40 units,
- The objective function to maximize savings (profit) is \(500x + 1000y\).

Given these variables and constraints, we aim to find the values of \(x\) and \(y\) that maximize the objective function while satisfying all given constraints.

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

```python
from gurobipy import *

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

# Define variables
x = m.addVar(name="solar_panels", lb=0)  # Acres of solar panels
y = m.addVar(name="windmills", lb=0)     # Acres of windmills

# Add constraints
m.addConstr(x + y <= 120, name="land_constraint")  # Total land constraint
m.addConstr(20*x + 40*y <= 2000, name="resource_constraint")  # Resource constraint

# Define the objective function to maximize savings (profit)
m.setObjective(500*x + 1000*y, GRB.MAXIMIZE)

# Optimize the model
m.optimize()

# Print results if an optimal solution is found
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Acres of solar panels: {x.x}")
    print(f"Acres of windmills: {y.x}")
    print(f"Maximum savings (profit): ${m.objVal:.2f}")
else:
    print("No optimal solution found. The model may be infeasible.")
```