To tackle this problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the given information.

Let's denote:
- $x_1$ as the number of rafts,
- $x_2$ as the number of kayaks.

The objective is to maximize profit. Given that the profit per raft is $45 and the profit per kayak is $55, the objective function can be written as:
\[ \text{Maximize: } 45x_1 + 55x_2 \]

Now, let's consider the constraints:
1. **Space Constraint**: Each raft takes 10 sq ft of space, and each kayak takes 12 sq ft. The total available space is 400 sq ft.
   - $10x_1 + 12x_2 \leq 400$
2. **Budget Constraint**: The warehouse has a budget of $10000, with each raft costing $200 and each kayak costing $250.
   - $200x_1 + 250x_2 \leq 10000$
3. **Percentage of Rafts Constraint**: At least 55% of all items in stock must be rafts.
   - $\frac{x_1}{x_1 + x_2} \geq 0.55$
4. **Non-Negativity Constraints**: The number of rafts and kayaks cannot be negative.
   - $x_1 \geq 0, x_2 \geq 0$

Given these definitions, the symbolic representation of our problem is:
```json
{
  'sym_variables': [('x1', 'number of rafts'), ('x2', 'number of kayaks')],
  'objective_function': '45*x1 + 55*x2',
  'constraints': [
    '10*x1 + 12*x2 <= 400',
    '200*x1 + 250*x2 <= 10000',
    'x1 / (x1 + x2) >= 0.55',
    'x1 >= 0',
    'x2 >= 0'
  ]
}
```

To implement this in Gurobi using Python, we'll write the following code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(name='rafts', vtype=GRB.INTEGER, lb=0)
x2 = m.addVar(name='kayaks', vtype=GRB.INTEGER, lb=0)

# Set the objective function
m.setObjective(45*x1 + 55*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(10*x1 + 12*x2 <= 400, name='space_constraint')
m.addConstr(200*x1 + 250*x2 <= 10000, name='budget_constraint')

# Percentage constraint requires a bit of manipulation to avoid division by zero and non-linear terms
# We can rearrange the percentage constraint as follows: x1 >= 0.55*(x1 + x2)
# Simplifying, we get: 0.45*x1 >= 0.55*x2
# Which further simplifies to: 9*x1 >= 11*x2 (after multiplying both sides by 20 to eliminate decimals)
m.addConstr(9*x1 >= 11*x2, name='percentage_constraint')

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Rafts: {x1.x}")
    print(f"Kayaks: {x2.x}")
    print(f"Maximum Profit: {m.objVal}")
else:
    print("No optimal solution found")
```