## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'ferns' and 'potato vines', which we can denote as $x_1$ and $x_2$ respectively. The objective function to minimize is $9x_1 + 7x_2$. The constraints are:
- $2x_1 + 9x_2 \geq 6$ (total water need)
- $x_1 + x_2 \geq 33$ (total resilience index)
- $x_1 + 3x_2 \geq 17$ (total beauty rating)
- $2x_1 + 2x_2 \geq 20$ (total cost)
- $x_1 - 7x_2 \geq 0$ (relationship between ferns and potato vines)
- $2x_1 + 9x_2 \leq 24$ (water need upper bound)
- $x_1 + x_2 \leq 90$ (resilience index upper bound)
- $x_1 + 3x_2 \leq 32$ (beauty rating upper bound)
- $2x_1 + 2x_2 \leq 47$ (cost upper bound)
- $x_1, x_2$ are integers.

## Step 2: Convert the problem into a Gurobi-compatible format
We need to express the problem in a way that Gurobi can understand. This involves defining the model, adding variables, the objective function, and the constraints.

## 3: Write the Gurobi code
```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(name="ferns", vtype=gurobi.GRB.INTEGER)
x2 = model.addVar(name="potato_vines", vtype=gurobi.GRB.INTEGER)

# Set the objective function
model.setObjective(9 * x1 + 7 * x2, gurobi.GRB.MINIMIZE)

# Add constraints
model.addConstr(2 * x1 + 9 * x2 >= 6, name="water_need")
model.addConstr(x1 + x2 >= 33, name="resilience_index")
model.addConstr(x1 + 3 * x2 >= 17, name="beauty_rating")
model.addConstr(2 * x1 + 2 * x2 >= 20, name="cost")
model.addConstr(x1 - 7 * x2 >= 0, name="relationship")
model.addConstr(2 * x1 + 9 * x2 <= 24, name="water_need_upper")
model.addConstr(x1 + x2 <= 90, name="resilience_index_upper")
model.addConstr(x1 + 3 * x2 <= 32, name="beauty_rating_upper")
model.addConstr(2 * x1 + 2 * x2 <= 47, name="cost_upper")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objVal)
    print("Ferns: ", x1.varValue)
    print("Potato Vines: ", x2.varValue)
else:
    print("The model is infeasible")
```

## Step 4: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'ferns'), ('x2', 'potato vines')],
    'objective_function': '9*x1 + 7*x2',
    'constraints': [
        '2*x1 + 9*x2 >= 6',
        'x1 + x2 >= 33',
        'x1 + 3*x2 >= 17',
        '2*x1 + 2*x2 >= 20',
        'x1 - 7*x2 >= 0',
        '2*x1 + 9*x2 <= 24',
        'x1 + x2 <= 90',
        'x1 + 3*x2 <= 32',
        '2*x1 + 2*x2 <= 47'
    ]
}
```