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

Let's denote:
- $x_1$ as the number of squash plants,
- $x_2$ as the number of cherry trees.

The objective function to minimize is given by:
\[2.74x_1^2 + 5.48x_1x_2 + 8.46x_2^2 + 5.27x_1 + 2.9x_2\]

The constraints are as follows:
1. Beauty rating constraint: $33x_1 + 25x_2 \geq 101$ (minimum combined beauty rating) and $33x_1 + 25x_2 \leq 206$ (maximum combined beauty rating).
2. Squared beauty rating constraint: $(33x_1)^2 + (25x_2)^2 \geq 101$.
3. Planting space constraints: 
   - Minimum space: $11x_1 + 15x_2 \geq 143$ (in square inches, assuming the conversion is not needed as per the problem statement).
   - Maximum space constraint isn't directly given but we have a squared term: $(11x_1)^2 + (15x_2)^2 \leq 159$.
4. Water need constraints:
   - Minimum water need: $11x_1 + 30x_2 \geq 75$.
5. Relationship between squash plants and cherry trees: $3x_1 - x_2 \geq 0$.
6. Integer constraints: $x_1, x_2 \in \mathbb{Z}^+$ (non-negative integers).

Now, let's represent the problem symbolically:
```json
{
    'sym_variables': [('x1', 'squash plants'), ('x2', 'cherry trees')],
    'objective_function': '2.74*x1**2 + 5.48*x1*x2 + 8.46*x2**2 + 5.27*x1 + 2.9*x2',
    'constraints': [
        '33*x1 + 25*x2 >= 101', 
        '33*x1 + 25*x2 <= 206', 
        '(33*x1)**2 + (25*x2)**2 >= 101', 
        '11*x1 + 15*x2 >= 143',
        '(11*x1)**2 + (15*x2)**2 <= 159',
        '11*x1 + 30*x2 >= 75',
        '3*x1 - x2 >= 0'
    ]
}
```

To solve this problem using Gurobi, we'll write the corresponding Python code. Note that you need to have Gurobi installed and configured properly in your environment.

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="squash_plants")
x2 = m.addVar(vtype=GRB.INTEGER, name="cherry_trees")

# Objective function
m.setObjective(2.74*x1**2 + 5.48*x1*x2 + 8.46*x2**2 + 5.27*x1 + 2.9*x2, GRB.MINIMIZE)

# Constraints
m.addConstr(33*x1 + 25*x2 >= 101, name="min_beauty_rating")
m.addConstr(33*x1 + 25*x2 <= 206, name="max_beauty_rating")
m.addConstr((33*x1)**2 + (25*x2)**2 >= 101, name="squared_min_beauty_rating")
m.addConstr(11*x1 + 15*x2 >= 143, name="min_planting_space")
m.addConstr((11*x1)**2 + (15*x2)**2 <= 159, name="max_squared_planting_space")
m.addConstr(11*x1 + 30*x2 >= 75, name="min_water_need")
m.addConstr(3*x1 - x2 >= 0, name="relationship_constraint")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Squash plants: {x1.x}")
    print(f"Cherry trees: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```