## 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 amount of indoor soil to purchase
- $x_2$ as the amount of outdoor soil to purchase

The objective is to minimize the total cost. The cost of indoor soil is $2 per unit, and the cost of outdoor soil is $3 per unit. Therefore, the objective function can be represented as:

Minimize $2x_1 + 3x_2$

The gardener requires at least 80 units of compost and 70 units of loam per week. The indoor soil contains 2 units of compost and 3 units of loam per unit, and the outdoor soil contains 4 units of compost and 6 units of loam per unit. This gives us the following constraints:

- Compost requirement: $2x_1 + 4x_2 \geq 80$
- Loam requirement: $3x_1 + 6x_2 \geq 70$

Also, $x_1 \geq 0$ and $x_2 \geq 0$ because the amount of soil to purchase cannot be negative.

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'indoor soil'), ('x2', 'outdoor soil')],
    'objective_function': '2*x1 + 3*x2',
    'constraints': [
        '2*x1 + 4*x2 >= 80',
        '3*x1 + 6*x2 >= 70',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

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

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

    # Define the objective function
    model.setObjective(2*x1 + 3*x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(2*x1 + 4*x2 >= 80, name='compost_requirement')
    model.addConstr(3*x1 + 6*x2 >= 70, name='loam_requirement')

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal amount of indoor soil: {x1.varValue}")
        print(f"Optimal amount of outdoor soil: {x2.varValue}")
        print(f"Minimum cost: {model.objVal}")
    else:
        print("The model is infeasible")

solve_soil_mix_problem()
```