## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the amount invested in housing
- $x_2$ represents the amount invested in roads

## Step 2: Formulate the objective function
The objective is to maximize the total profit earned from the investment. Given that each dollar invested in housing yields a $0.95 profit and each dollar invested in roads yields a $0.32 profit, the objective function can be written as:
\[ \text{Maximize:} \quad 0.95x_1 + 0.32x_2 \]

## 3: Define the constraints
The constraints based on the problem description are:
1. The total budget is up to $3500: $x_1 + x_2 \leq 3500$
2. No less than $750 must be invested in housing: $x_1 \geq 750$
3. No less than 24% of all money invested must be in roads: $x_2 \geq 0.24(x_1 + x_2)$

## 4: Convert the constraints into standard form
1. $x_1 + x_2 \leq 3500$
2. $x_1 \geq 750$
3. $x_2 \geq 0.24x_1 + 0.24x_2$ which simplifies to $0.76x_2 \geq 0.24x_1$ or $x_2 \geq \frac{0.24}{0.76}x_1$ which further simplifies to $x_2 \geq \frac{6}{19}x_1$

## 5: Symbolic Representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'housing'), ('x2', 'roads')],
'objective_function': '0.95*x1 + 0.32*x2',
'constraints': [
    'x1 + x2 <= 3500',
    'x1 >= 750',
    'x2 >= (6/19)*x1'
]
}
```

## 6: Gurobi Code
Now, let's write the Gurobi code in Python to solve this linear programming problem:
```python
import gurobipy as gp

# Create a new model
model = gp.Model("Investment_Problem")

# Define the variables
x1 = model.addVar(name="housing", lb=0)  # Investment in housing
x2 = model.addVar(name="roads", lb=0)     # Investment in roads

# Objective function: Maximize 0.95*x1 + 0.32*x2
model.setObjective(0.95*x1 + 0.32*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 + x2 <= 3500, name="budget_constraint")
model.addConstr(x1 >= 750, name="housing_min_constraint")
model.addConstr(x2 >= (6/19)*x1, name="roads_min_constraint")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal Solution:")
    print(f"Investment in housing: ${x1.varValue:.2f}")
    print(f"Investment in roads: ${x2.varValue:.2f}")
    print(f"Total Profit: ${model.objVal:.2f}")
else:
    print("The model is infeasible.")
```