## Step 1: Define the symbolic representation of the problem

Let's define the symbolic variables:
- $x_1$ represents the dollars invested in clean water
- $x_2$ represents the dollars invested in electricity

The objective function to maximize total profit is: $1.9x_1 + 2.3x_2$

The constraints are:
1. Budget constraint: $x_1 + x_2 \leq 5500$
2. Clean water investment constraint: $x_1 \geq 1000$
3. Electricity investment constraint: $x_2 \geq 0.3(x_1 + x_2)$

## Step 2: Convert the constraints into standard form

1. Budget constraint remains: $x_1 + x_2 \leq 5500$
2. Clean water investment constraint remains: $x_1 \geq 1000$
3. Electricity investment constraint is rewritten as: $0.7x_2 - 0.3x_1 \geq 0$ or $-0.3x_1 + 0.7x_2 \geq 0$

## 3: Express the problem in the required JSON format

```json
{
    'sym_variables': [('x1', 'clean water'), ('x2', 'electricity')], 
    'objective_function': '1.9*x1 + 2.3*x2', 
    'constraints': [
        'x1 + x2 <= 5500', 
        'x1 >= 1000', 
        '-0.3*x1 + 0.7*x2 >= 0'
    ]
}
```

## 4: Implement the problem in Gurobi Python

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="clean_water", lb=0)  # Investment in clean water
    x2 = model.addVar(name="electricity", lb=0)  # Investment in electricity

    # Objective function: Maximize 1.9*x1 + 2.3*x2
    model.setObjective(1.9 * x1 + 2.3 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 5500, name="budget_constraint")
    model.addConstr(x1 >= 1000, name="clean_water_constraint")
    model.addConstr(-0.3 * x1 + 0.7 * x2 >= 0, name="electricity_constraint")

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Investment in clean water: ${x1.varValue:.2f}")
        print(f"Investment in electricity: ${x2.varValue:.2f}")
        print(f"Total Profit: ${model.objVal:.2f}")
    else:
        print("The model is infeasible")

solve_infrastructure_investment()
```