## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the kilograms of mixture A to be made.
- $x_2$ represents the kilograms of mixture B to be made.

## Step 2: Formulate the objective function
The objective function is to maximize profit. Given that a kilogram of mixture A yields a profit of $30 and a kilogram of mixture B yields a profit of $40, the objective function can be written as:
\[ \text{Maximize:} \quad 30x_1 + 40x_2 \]

## 3: Define the constraints
The constraints are based on the availability of gummy bears and gummy worms:
- Mixture A is 75% gummy bears and 25% gummy worms.
- Mixture B is 40% gummy bears and 60% gummy worms.
- The store has 30 kilograms of gummy bears and 25 kilograms of gummy worms.

The constraints can be written as:
\[ 0.75x_1 + 0.40x_2 \leq 30 \] (gummy bears constraint)
\[ 0.25x_1 + 0.60x_2 \leq 25 \] (gummy worms constraint)
Also, $x_1 \geq 0$ and $x_2 \geq 0$ because the kilograms of mixtures cannot be negative.

## 4: Symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'kilograms of mixture A'), ('x2', 'kilograms of mixture B')],
'objective_function': '30*x1 + 40*x2',
'constraints': [
    '0.75*x1 + 0.40*x2 <= 30',
    '0.25*x1 + 0.60*x2 <= 25',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 5: Gurobi code
Now, let's write the Gurobi code in Python to solve this problem:
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="x1", lb=0, ub=gurobi.GRB.INFINITY, obj=30)
    x2 = model.addVar(name="x2", lb=0, ub=gurobi.GRB.INFINITY, obj=40)

    # Add constraints
    model.addConstr(x1 * 0.75 + x2 * 0.40 <= 30, name="gummy_bears")
    model.addConstr(x1 * 0.25 + x2 * 0.60 <= 25, name="gummy_worms")

    # Set the model objective to maximize
    model.setObjective(x1.obj * x1.var, gurobi.GRB.MAXIMIZE)

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution:")
        print(f"Mixture A: {x1.x:.2f} kg")
        print(f"Mixture B: {x2.x:.2f} kg")
        print(f"Max Profit: {model.objVal:.2f}")
    else:
        print("No optimal solution found.")

solve_mixture_problem()
```