To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. This involves defining variables and translating the objective function and constraints into algebraic terms.

Let's define:
- $x_1$ as the amount invested in housing.
- $x_2$ as the amount invested in roads.

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

The constraints are as follows:
1. The total budget is up to $3500, so \(x_1 + x_2 \leq 3500\).
2. No less than $750 must be invested in housing, so \(x_1 \geq 750\).
3. No less than 24% of all money invested must be in roads. This can be represented as \(x_2 \geq 0.24(x_1 + x_2)\).

Symbolic representation:
```json
{
  'sym_variables': [('x1', 'amount invested in housing'), ('x2', 'amount invested in roads')],
  'objective_function': '0.95*x1 + 0.32*x2',
  'constraints': ['x1 + x2 <= 3500', 'x1 >= 750', 'x2 >= 0.24*(x1 + x2)']
}
```

To solve this problem using Gurobi in Python, we will use the following code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(name="housing", lb=0)
x2 = m.addVar(name="roads", lb=0)

# Set the objective function
m.setObjective(0.95*x1 + 0.32*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 3500, name="budget")
m.addConstr(x1 >= 750, name="min_housing")
m.addConstr(x2 >= 0.24*(x1 + x2), name="min_roads")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Housing: {x1.x}")
    print(f"Roads: {x2.x}")
    print(f"Total Profit: {m.objVal}")
else:
    print("No optimal solution found")
```