To solve the gardener's soil mix problem, we first need to define the decision variables and the objective function. Let's denote the amount of indoor soil purchased as \(x_1\) and the amount of outdoor soil purchased as \(x_2\). The objective is to minimize the total cost, which can be represented as \(2x_1 + 3x_2\), since the indoor soil costs $2 per unit and the outdoor soil costs $3 per unit.

The gardener has two requirements: at least 80 units of compost and at least 70 units of loam per week. The indoor soil contains 2 units of compost and 3 units of loam, while the outdoor soil contains 4 units of compost and 6 units of loam. Therefore, we can formulate the constraints as follows:

1. Compost requirement: \(2x_1 + 4x_2 \geq 80\)
2. Loam requirement: \(3x_1 + 6x_2 \geq 70\)

Also, since the gardener cannot purchase a negative amount of soil, we have non-negativity constraints:

\(x_1 \geq 0\) and \(x_2 \geq 0\).

The problem can be summarized as a linear programming problem with the objective to minimize \(2x_1 + 3x_2\) subject to the above constraints.

Here's how we can implement this in Gurobi using Python:

```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(2*x1 + 3*x2, GRB.MINIMIZE)

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

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution: {x1.varName} = {x1.x}, {x2.varName} = {x2.x}")
    print(f"Total cost: {m.objVal}")
else:
    print("The model is infeasible")
```