To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. Let's define:

- $x_1$ as the number of acres for solar panels,
- $x_2$ as the number of acres for windmills.

The objective function aims to maximize savings, which is given by $500x_1 + 1000x_2$, since each acre of solar panels saves $500 and each acre of windmills profits $1000.

The constraints are:
1. The total land used does not exceed 120 acres: $x_1 + x_2 \leq 120$.
2. The total resources used do not exceed 2000 units: $20x_1 + 40x_2 \leq 2000$.
3. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$, since the number of acres for each cannot be negative.

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'acres of solar panels'), ('x2', 'acres of windmills')],
    'objective_function': '500*x1 + 1000*x2',
    'constraints': ['x1 + x2 <= 120', '20*x1 + 40*x2 <= 2000', 'x1 >= 0', 'x2 >= 0']
}
```

To solve this problem using Gurobi in Python, we can write the following code:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="acres_of_solar_panels")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="acres_of_windmills")

# Set the objective function
m.setObjective(500*x1 + 1000*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 120, "total_land")
m.addConstr(20*x1 + 40*x2 <= 2000, "total_resources")

# Optimize the model
m.optimize()

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