To solve the optimization problem described, we first need to convert the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the given information.

Let's define:
- $x_1$ as the number of blue jelly pouches,
- $x_2$ as the number of red jelly pouches.

The objective is to minimize the total cost, which can be represented by the objective function: $5x_1 + 7x_2$.

The constraints based on the mineral requirements are:
1. Calcium requirement: $2x_1 + 3x_2 \geq 30$
2. Potassium requirement: $x_1 + 2x_2 \geq 25$
3. Sodium requirement: $3x_1 + x_2 \geq 30$

Additionally, since we cannot purchase a negative number of jelly pouches, we have non-negativity constraints:
4. $x_1 \geq 0$
5. $x_2 \geq 0$

The symbolic representation of the problem is thus:

```json
{
    'sym_variables': [('x1', 'number of blue jelly pouches'), ('x2', 'number of red jelly pouches')],
    'objective_function': '5*x1 + 7*x2',
    'constraints': ['2*x1 + 3*x2 >= 30', 'x1 + 2*x2 >= 25', '3*x1 + x2 >= 30', 'x1 >= 0', 'x2 >= 0']
}
```

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

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="blue_jelly_pouches")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="red_jelly_pouches")

# Set the objective function
m.setObjective(5*x1 + 7*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(2*x1 + 3*x2 >= 30, "calcium_requirement")
m.addConstr(x1 + 2*x2 >= 25, "potassium_requirement")
m.addConstr(3*x1 + x2 >= 30, "sodium_requirement")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Blue jelly pouches: {x1.x}")
    print(f"Red jelly pouches: {x2.x}")
    print(f"Total cost: ${5*x1.x + 7*x2.x:.2f}")
else:
    print("No optimal solution found")
```