To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables and translating the objective function and constraints into algebraic terms.

Let's define:
- $x_1$ as the number of ferns,
- $x_2$ as the number of potato vines.

The objective function is to minimize: $9x_1 + 7x_2$

Given the constraints:
1. Water need for ferns: $2x_1$
2. Water need for potato vines: $9x_2$
3. Resilience index for ferns: $1x_1$
4. Resilience index for potato vines: $1x_2$
5. Beauty rating for ferns: $1x_1$
6. Beauty rating for potato vines: $3x_2$
7. Cost for ferns: $2x_1$
8. Cost for potato vines: $2x_2$

And the constraints are:
- Total water need at least 6 inches: $2x_1 + 9x_2 \geq 6$
- Combined resilience index at least 33: $1x_1 + 1x_2 \geq 33$
- Combined beauty rating at least 17: $1x_1 + 3x_2 \geq 17$
- Minimum spend at least $20: $2x_1 + 2x_2 \geq 20$
- Constraint on ferns and potato vines: $1x_1 - 7x_2 \geq 0$
- Maximum water need of 24 inches: $2x_1 + 9x_2 \leq 24$
- Combined resilience index at most 90: $1x_1 + 1x_2 \leq 90$
- Combined beauty rating at most 32: $1x_1 + 3x_2 \leq 32$
- Maximum spend of $47: $2x_1 + 2x_2 \leq 47$
- Integer constraints for ferns and potato vines: $x_1, x_2 \in \mathbb{Z}^+$

Now, translating this into the required symbolic representation format:

```json
{
    'sym_variables': [('x1', 'ferns'), ('x2', 'potato vines')],
    'objective_function': '9*x1 + 7*x2',
    'constraints': [
        '2*x1 + 9*x2 >= 6',
        '1*x1 + 1*x2 >= 33',
        '1*x1 + 3*x2 >= 17',
        '2*x1 + 2*x2 >= 20',
        '1*x1 - 7*x2 >= 0',
        '2*x1 + 9*x2 <= 24',
        '1*x1 + 1*x2 <= 90',
        '1*x1 + 3*x2 <= 32',
        '2*x1 + 2*x2 <= 47'
    ]
}
```

And here is the Gurobi code to solve this problem:

```python
from gurobipy import *

# Create a new model
model = Model("Optimization_Problem")

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

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

# Add constraints
model.addConstr(2*x1 + 9*x2 >= 6, "water_need_min")
model.addConstr(1*x1 + 1*x2 >= 33, "resilience_index_min")
model.addConstr(1*x1 + 3*x2 >= 17, "beauty_rating_min")
model.addConstr(2*x1 + 2*x2 >= 20, "min_spend")
model.addConstr(1*x1 - 7*x2 >= 0, "fern_vine_constraint")
model.addConstr(2*x1 + 9*x2 <= 24, "water_need_max")
model.addConstr(1*x1 + 1*x2 <= 90, "resilience_index_max")
model.addConstr(1*x1 + 3*x2 <= 32, "beauty_rating_max")
model.addConstr(2*x1 + 2*x2 <= 47, "max_spend")

# Optimize the model
model.optimize()

# Print solution
if model.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Ferns: {x1.x}")
    print(f"Potato Vines: {x2.x}")
else:
    print("No optimal solution found")
```