## Step 1: Define the symbolic representation of the problem
The problem involves two types of objects: solar panels and windmills. Let's denote the acres of solar panels as $x_1$ and the acres of windmills as $x_2$. The objective is to maximize savings, which is $500x_1 + 1000x_2$. There are two constraints: the total land available is 120 acres, and the total resources available are 2000 units.

## Step 2: Formulate the objective function and constraints in symbolic notation
- The objective function to maximize is $500x_1 + 1000x_2$.
- The constraints are:
  1. $x_1 + x_2 \leq 120$ (land constraint)
  2. $20x_1 + 40x_2 \leq 2000$ (resource constraint)
  3. $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity constraints)

## 3: Convert the problem into a Gurobi-compatible format
We need to define the variables, the objective function, and the constraints using Gurobi's Python API.

## 4: Write down the symbolic representation in the required format
```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']
}
```

## 5: Implement the problem in Gurobi Python code
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name='x1', lb=0, ub=gurobi.GRB.INFINITY)  # acres of solar panels
    x2 = model.addVar(name='x2', lb=0, ub=gurobi.GRB.INFINITY)  # acres of windmills

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

    # Add constraints
    model.addConstr(x1 + x2 <= 120, name='land_constraint')
    model.addConstr(20 * x1 + 40 * x2 <= 2000, name='resource_constraint')

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: x1 = {x1.varValue}, x2 = {x2.varValue}")
        print(f"Max savings: {model.objVal}")
    else:
        print("The model is infeasible")

solve_optimization_problem()
```