To solve the given optimization problem, we need to translate the provided natural language description into a mathematical model that can be represented in Gurobi code. The objective function to minimize is \(9.81 \times \text{boxwoods} + 3.17 \times \text{lilies}\), subject to several constraints related to cost, water need, and integer requirements for both boxwoods and lilies.

Given the problem description:
- We have two variables: `boxwoods` and `lilies`.
- The objective function coefficients are \(9.81\) for `boxwoods` and \(3.17\) for `lilies`.
- Constraints include:
  - Cost constraints: Each `boxwood` costs $5, and each `lily` costs $1.
  - Water need constraints: Each `boxwood` requires 7 inches of water per week, and each `lily` also requires 7 inches of water per week.
  - Minimum spend constraint: The total spend on `boxwoods` and `lilies` must be at least $31.
  - Minimum water need constraint: The total water need from `boxwoods` and `lilies` should be at least 9 inches per week.
  - A specific linear constraint: \(7 \times \text{boxwoods} - 2 \times \text{lilies} \geq 0\).
  - Maximum spend constraint: The total spend on `boxwoods` and `lilies` cannot exceed $46.
  - Maximum water need constraint: The total water need from `boxwoods` and `lilies` should not exceed 35 inches per week.
  - Both `boxwoods` and `lilies` must be whole numbers.

Let's define the problem mathematically:
- Objective function: Minimize \(9.81 \times \text{boxwoods} + 3.17 \times \text{lilies}\)
- Subject to:
  - \(5 \times \text{boxwoods} + 1 \times \text{lilies} \geq 31\) (Minimum spend)
  - \(7 \times \text{boxwoods} + 7 \times \text{lilies} \geq 9\) (Minimum water need)
  - \(7 \times \text{boxwoods} - 2 \times \text{lilies} \geq 0\) (Specific linear constraint)
  - \(5 \times \text{boxwoods} + 1 \times \text{lilies} \leq 46\) (Maximum spend)
  - \(7 \times \text{boxwoods} + 7 \times \text{lilies} \leq 35\) (Maximum water need)
  - \(\text{boxwoods}, \text{lilies} \in \mathbb{Z}^+\) (Both are whole numbers)

Now, let's represent this problem in Gurobi code:

```python
from gurobipy import *

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

# Define variables
boxwoods = m.addVar(vtype=GRB.INTEGER, name="boxwoods")
lilies = m.addVar(vtype=GRB.INTEGER, name="lilies")

# Objective function
m.setObjective(9.81 * boxwoods + 3.17 * lilies, GRB.MINIMIZE)

# Constraints
m.addConstr(5 * boxwoods + 1 * lilies >= 31, "Minimum_Spend")
m.addConstr(7 * boxwoods + 7 * lilies >= 9, "Minimum_Water_Need")
m.addConstr(7 * boxwoods - 2 * lilies >= 0, "Specific_Linear_Constraint")
m.addConstr(5 * boxwoods + 1 * lilies <= 46, "Maximum_Spend")
m.addConstr(7 * boxwoods + 7 * lilies <= 35, "Maximum_Water_Need")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Boxwoods: {boxwoods.x}")
    print(f"Lilies: {lilies.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```