## Step 1: Define the symbolic representation of the problem
Let's denote the number of black shoes as $x_1$ and the number of blue shoes as $x_2$. The objective is to maximize the net profit, which can be represented as $-3x_1 + 6x_2$.

## Step 2: Identify the constraints
The constraints based on the problem description are:
- $x_1 \leq 150$ (at most 150 black shoes)
- $x_2 \leq 100$ (at most 100 blue shoes)
- $x_1 \geq 75$ (at least 75 black shoes)
- $x_2 \geq 60$ (at least 60 blue shoes)
- $x_1 + x_2 \geq 125$ (minimum of 125 shoes must be shipped)

## 3: Symbolic representation in JSON format
```json
{
    'sym_variables': [('x1', 'black shoes'), ('x2', 'blue shoes')],
    'objective_function': '-3x1 + 6x2',
    'constraints': [
        'x1 <= 150',
        'x2 <= 100',
        'x1 >= 75',
        'x2 >= 60',
        'x1 + x2 >= 125'
    ]
}
```

## 4: Convert the problem into Gurobi code
To solve this linear programming problem using Gurobi, we will use the Gurobi Python API.

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(lb=0, ub=150, name="black_shoes")
    x2 = model.addVar(lb=0, ub=100, name="blue_shoes")

    # Set bounds according to demand
    x1.lb = 75
    x2.lb = 60

    # Objective function: maximize -3x1 + 6x2
    model.setObjective(-3 * x1 + 6 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 <= 150, name="black_shoes上限")
    model.addConstr(x2 <= 100, name="blue_shoes上限")
    model.addConstr(x1 >= 75, name="black_shoes下限")
    model.addConstr(x2 >= 60, name="blue_shoes下限")
    model.addConstr(x1 + x2 >= 125, name="total_shoes")

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Black shoes: {x1.x}, Blue shoes: {x2.x}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_shoe_problem()
```