## Symbolic Representation

To solve the given problem, we first need to convert the natural language description into a symbolic representation.

Let's define the variables:
- $x_1$ as the number of fruit gummy bears packets
- $x_2$ as the number of sour gummy bears packets

The objective is to maximize profit. Given that the profit per packet of fruit gummy bears is $1 and the profit per packet of sour gummy bears is $1.25, the objective function can be written as:

Maximize: $1x_1 + 1.25x_2$

The constraints are:
- Each packet of fruit gummy bears takes 10 minutes to make and each packet of sour gummy bears takes 15 minutes to make, with 2000 minutes available: $10x_1 + 15x_2 \leq 2000$
- The store can make at most 120 fruit gummy bears packets: $x_1 \leq 120$
- The store can make at most 70 sour gummy bears packets: $x_2 \leq 70$
- Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'fruit gummy bears packets'), ('x2', 'sour gummy bears packets')],
    'objective_function': '1*x1 + 1.25*x2',
    'constraints': [
        '10*x1 + 15*x2 <= 2000',
        'x1 <= 120',
        'x2 <= 70',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="fruit_gummy_bears", lb=0, ub=120, vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(name="sour_gummy_bears", lb=0, ub=70, vtype=gurobi.GRB.CONTINUOUS)

    # Objective function: Maximize 1*x1 + 1.25*x2
    model.setObjective(1*x1 + 1.25*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(10*x1 + 15*x2 <= 2000, name="time_constraint")
    model.addConstr(x1 <= 120, name="fruit_gummy_bears_limit")
    model.addConstr(x2 <= 70, name="sour_gummy_bears_limit")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution:")
        print(f"Fruit gummy bears packets: {x1.varValue}")
        print(f"Sour gummy bears packets: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_gummy_bears_problem()
```