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

Let's denote:
- $x_1$ as the amount of the first mix (in kg) to be prepared,
- $x_2$ as the amount of the second mix (in kg) to be prepared.

The objective is to maximize profit, given that the profit per kg of the first mix is $12 and the profit per kg of the second mix is $15. Therefore, the objective function can be represented as $12x_1 + 15x_2$.

## Step 2: Identify the constraints

The store has on hand 100 kg of honey-roasted almonds and 150 kg of chocolate-covered almonds. The first mix contains 30% honey-roasted almonds and 70% chocolate-covered almonds, and the second mix contains 40% honey-roasted almonds and 60% chocolate-covered almonds.

The constraints can be represented as follows:
1. Honey-roasted almonds constraint: $0.30x_1 + 0.40x_2 \leq 100$
2. Chocolate-covered almonds constraint: $0.70x_1 + 0.60x_2 \leq 150$
3. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## 3: Symbolic representation in JSON format

```json
{
    'sym_variables': [('x1', 'first mix'), ('x2', 'second mix')],
    'objective_function': '12*x1 + 15*x2',
    'constraints': [
        '0.30*x1 + 0.40*x2 <= 100',
        '0.70*x1 + 0.60*x2 <= 150',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 4: Convert the problem into Gurobi code

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name='first_mix', lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name='second_mix', lb=0, ub=gurobi.GRB.INFINITY)

    # Objective function: maximize 12*x1 + 15*x2
    model.setObjective(12*x1 + 15*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(0.30*x1 + 0.40*x2 <= 100, name='honey_roasted_almonds')
    model.addConstr(0.70*x1 + 0.60*x2 <= 150, name='chocolate_covered_almonds')

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: x1 = {x1.varValue}, x2 = {x2.varValue}")
        print(f"Maximum profit: {model.objVal}")
    else:
        print("The model is infeasible")

solve_almond_mix_problem()
```